Introduction

Energy drinks are non-alcoholic beverages that contain high levels of caffeine and other stimulants, such as taurine, guarana and ginseng. Popular brands include Red Bull, Mother and V. Since 2006 energy drink consumption has more than doubled, with global consumption amounting to 11.5 billion litres per year [1]. The largest segment of consumers are young adults, who account for about two thirds of the energy drinks market [2]. Their main reasons for consuming energy drinks are to increase alertness, combat fatigue, improve academic or sport performance, and mix with alcohol at parties [3, 4].

Despite evidence that consuming energy drinks can improve physical endurance [5] and cognitive performance [6], energy drink consumption has more often been associated with a host of negative physical and mental health consequences. Reported adverse effects include symptoms ranging in severity from headaches to heart palpitations, renal failure, seizures, and in rare cases death [7, 8]. A recent systematic review concluded that energy drink use is positively associated with a range of mental health outcomes, in particular anxiety and depression, as well as stress, PTSD and substance abuse [9]. In addition, because caffeine has similar properties to other drugs, energy drinks can be addictive. While the occasional energy drink is not problematic, it has been reported that some individuals consume four or more energy drinks per day [4]. Such excessive intake can lead to the development of tolerance and serious withdrawal symptoms upon cessation [10].

These negative health outcomes have led to a recognised need to reduce energy drink consumption. Efforts have focused on introducing tighter regulations, such as limiting the caffeine content in energy drinks, providing cautionary information on energy drink labels and restricting energy drink advertising [11]. Recommendations have also been put forward to limit the access and availability of energy drinks [12]. Energy drinks are ubiquitous in our contemporary environment; they are available and accessible from shops, petrol stations and vending machines 24/7. More recently there have been calls to impose a tax on energy drinks [8] as happens for alcohol and tobacco, and has been introduced for sugar sweetened beverages in some countries (e.g., Mexico). However, banning, restricting or taxing energy drinks does not address the underlying mechanisms that drive their consumption; nor does it empower people to regulate their own intake. The present study focused on one possible mechanism underlying energy drink consumption, namely biased decision-making processes.

An influential theory of addiction that emphasises biased decision-making as a key driver of consumption is incentive sensitisation theory [13, 14]. According to this theory, appetitive cues in the environment acquire motivational properties, or incentive salience, through a process of classical conditioning (repeated association between the cue and intake of the substance). As a result, appetitive stimuli come to be perceived as attractive and ‘wanted’. Consequently, they capture attention (attentional bias), and elicit an action tendency to approach (approach bias) and consume the substance. The activation of these processes occurs automatically, and drives consumption without necessary conscious awareness. In so doing, attentional and approach biases contribute to the development and maintenance of substance use and addiction.

In support of incentive sensitisation theory, attentional and approach biases have been demonstrated for a range of appetitive substances, including alcohol [15, 16], tobacco [17, 18], drugs [19, 20] and chocolate [21, 22]. A handful of studies has also shown an attentional bias for caffeine. Stafford and colleagues [23–25] found an attentional bias for caffeine-related stimuli among moderate and heavy coffee drinkers. To date there have been no reports of an approach bias for caffeine. Further, as yet, attentional and approach biases for energy drinks have not been investigated.

Research has further shown that attentional and approach biases are malleable and can be manipulated through targeted training, known as cognitive bias modification. Training individuals to avoid appetitive stimuli by diverting their attention or making avoidance movements has been shown to reduce attentional and approach biases, respectively. Such cognitive bias modification effects have been reported for alcohol, tobacco and chocolate [22, 26–30]. In addition, there is some evidence that a reduction in bias can produce a corresponding reduction in consumption in terms of lower intake in laboratory-based taste tests [22, 28, 31–33]. However, many studies have not found any effect of cognitive bias modification on consumption [34–37]. This lack of robust effects of cognitive bias modification on consumption is reflected in recent reviews on attentional [38] and approach bias modification [39] for a range of appetitive stimuli.

The aim of the present study was to test the prediction derived from incentive sensitisation theory that energy drink consumption is in part driven by biased decision-making processes. In two experiments we investigated whether regular energy drink consumers exhibit attentional (Experiment 1) and approach (Experiment 2) biases for energy drink cues. As a next step we targeted these mechanisms through cognitive bias modification to determine whether they could be reduced. Finally, we examined the effect of such modification on energy drink consumption. If shown to be successful, cognitive bias modification could have important implications for reducing excessive energy drink consumption and its associated adverse physical and mental health consequences. Accordingly, the following a priori hypotheses were tested: (1) regular energy drink consumers would show attentional and approach biases for energy drink cues; (2) participants trained to avoid energy drink cues would show a decrease in attentional/approach bias, whereas participants trained to attend to/approach such cues would show an increase in bias; and (3) participants trained to avoid energy drink cues would consume less of the energy drinks in a taste test than participants trained to attend to/approach such cues.

Experiment 1 Methods

Experiment 1 Results

The data set for Experiment 1 is available in supplementary information (S1 Data set).

Sample characteristics

Most participants consumed energy drinks weekly (46%), fortnightly (28%) or every few days (21%), with a minority consuming them every day (6%). On the days on which participants did consume energy drinks, they drank on average 1.44 (SD = .66) cans. The maximum number of energy drinks consumed on any one day ranged from 1 to 20 (M = 3.67, SD = 2.76). The most popular brand of energy drink was Red Bull (43%), followed by Mother (28%) and V (12%). The top three reasons given for consuming energy drinks were needing energy, combatting fatigue, and to mix with alcohol at parties. Table 1 shows that the experimental groups (attend, avoid) did not differ on habitual energy drink consumption. Nor did they differ on age.

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Table 1. Means (SDs) for number of energy drinks consumed and age in Experiment 1.

https://doi.org/10.1371/journal.pone.0226387.t001

Attentional bias modification

To assess the effect of the attentional training, we compared response times on critical trials at post-training with those at pre-training. For each assessment phase, an attentional bias score was calculated by subtracting the mean response times to probes that replaced energy drink pictures from the mean response times to probes that replaced soft drink pictures. A positive score indicates an attentional bias toward energy drinks and a negative score a bias away from energy drinks.

The attentional bias scores were analysed by a 2 (training condition: attend, avoid) × 2 (time: pre-training, post-training) mixed model ANOVA. There was a significant main effect of training condition, F(1, 114) = 3.97, p < .05, η_p² = .03, whereby the attend group (M = 5.57 ms) showed a greater attentional bias for energy drinks than the avoid group (M = 3.71 ms), and no significant main effect of time, F(1, 114) = .52, p = .471. Importantly, there was a significant training condition × time interaction, F(1, 114) = 6.47, p < .05, η_p² = .05. There was a significant decrease in attentional bias scores from pre- to post-training in the avoid group, t(57) = 2.14, p < .05, d = .28, with the attentional bias completely negated, and an increase in the attend group that was not statistically significant, t(57) = 1.41, p = .164 (Fig 1).

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Fig 1. Mean attentional bias scores (with standard errors) for the training conditions (attend, avoid) at pre- and post-training in Experiment 1; * p < .05.

https://doi.org/10.1371/journal.pone.0226387.g001

Energy drink consumption

The distributions for energy drink and soft drink consumption were near-normal (skewness: ± 1; kurtosis: ± 2). A 2 (training condition: attend, avoid) × 2 (drink type: energy drink, soft drink) mixed model ANOVA examined the effect of attentional bias modification on energy drink consumption. There was a significant main effect of drink type, F(1, 114) = 9.75, p < .01, η_p² = .08, whereby participants consumed more of the energy drinks (M = 131 ml) than the soft drinks (M = 109 ml). However, there was no significant main effect of training condition, F(1, 114) = .49, p = .488, nor significant training condition × time interaction, F(1, 114) = .19, p = .664. Contrary to prediction, participants in the avoid condition consumed slightly more of the energy drinks than those in the attend condition (Table 2).

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Table 2. Mean beverage consumption in ml (SD in parentheses) for the attend and avoid conditions in Experiment 1.

https://doi.org/10.1371/journal.pone.0226387.t002

Experiment 1 Discussion

The findings of Experiment 1 demonstrate for the first time an attentional bias for energy drink cues in regular energy drink consumers. In addition to demonstrating the existence of such a bias, we showed that it can also be altered. Attentional bias modification eliminated the initial attentional bias in the avoid condition where participants were trained to direct attention away from energy drink pictures. However, the observed reduction in attentional bias in the avoid group was not accompanied by a lower intake of energy drinks in the taste test.

Earlier studies in the addiction literature focused on attentional bias as one aspect of biased decision-making processes. Over recent years, the focus has shifted to approach bias. It has been argued that approach bias is likely to be a more important contributor to consumption because, unlike attentional bias, it not only has a cognitive component, but also an additional behavioural component (i.e., reaching towards the substance cues) [44]. Although approach bias has been less widely researched than attentional bias, it shows a more consistent link with consumption. A recent review of approach bias modification across several consumption domains (alcohol, cigarettes, food) concluded that approach bias modification can reduce consumption if the approach bias is successfully retrained [39]. Thus it is possible that altering approach biases for energy drink cues may be more effective than attentional bias modification in reducing energy drink consumption. This was investigated in Experiment 2.

Experiment 2 Methods

Experiment 2 Results

The data set for Experiment 2 is available in supplementary information (S2 Data set).

Sample characteristics

As in Experiment 1, most participants consumed energy drinks fortnightly (33%), weekly (32%) or every few days (27%), with 8% consuming them every day. On the days on which participants consumed energy drinks, they drank on average 1.45 (SD = .58) cans. The maximum number of energy drinks consumed on any one day ranged from 1 to 6 (M = 2.93, SD = 1.40). Participants’ preferred brand of energy drink was again Red Bull (44%), followed by V (21%) and Mother (18%). The top three reasons for consuming energy drinks were combatting fatigue, needing energy and studying. As in Experiment 1, the experimental groups did not differ on habitual energy drink consumption nor age (Table 3).

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Table 3. Means (SDs) for number of energy drinks consumed and age in Experiment 2.

https://doi.org/10.1371/journal.pone.0226387.t003

Approach bias

To determine an approach bias for energy drink cues, we compared response times of trials in which participants pulled the joystick in response to energy drinks pictures with those in which participants pushed the joystick in response to such pictures in the pre-training phase. Incorrect responses (3.16%) and outlying response times (± 2.5 SD from the mean) (2.39%) were eliminated. Participants were faster to pull (M = 800 ms) than to push (M = 819 ms) the joystick in response to energy drink pictures, t(109) = 2.21, p < .05, d = .21, indicative of an approach bias for energy drinks, the extent of which did not correlate with self-reported frequency of energy drink consumption, r = .01, p = .927. A similar analysis conducted on the pull (M = 796 ms) and push (M = 819 ms) responses to soft drink pictures showed an approach bias also for soft drinks, t(109) = 2.91, p < .01, d = .28.

Approach bias modification

To examine the effect of the approach-avoidance training, we compared response times to energy drink pictures at post-training with those at pre-training. For each assessment phase, an approach bias score was calculated by subtracting the mean response times of trials in which participants pulled the joystick in response to energy drink pictures from the mean response times of trials in which participants pushed the joystick in response to such pictures. A positive score indicates an approach bias toward energy drinks and a negative score an avoidance bias away from energy drinks.

The approach bias scores were analysed by a 2 (training condition: approach, avoid) × 2 (time: pre-training, post-training) mixed model ANOVA. There were no significant main effects of training condition, F(1, 108) = 1.30, p = .257, or time, F(1, 108) = .63, p = .430. However, there was a significant training condition × time interaction, F(1, 108) = 5.63, p < .05, η_p² = .05. There was a significant decrease in approach bias scores from pre- to post-training in the avoid group, t(54) = 2.55, p < .05, d = .35, with the approach bias completely negated, but no significant increase in the approach group, t(54) = 1.01, p = .318 (Fig 2). In contrast, a parallel analysis conducted on the approach bias scores for soft drinks showed that there were no significant main effects of training condition, F(1, 108) = .70, p = .405, or time, F(1, 108) = .51, p = .476, nor a significant training condition × time interaction, F(1, 108) = .05, p = .829.

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Fig 2. Mean approach bias scores (with standard errors) for the training conditions (approach, avoid) at pre- and post-training in Experiment 2; * p < .05.

https://doi.org/10.1371/journal.pone.0226387.g002

Energy drink consumption

Skewness and kurtosis indices indicated that energy drink and soft drink consumption were within acceptable limits of normally distributed data (skewness: ± 1; kurtosis: ± 2). A 2 (training condition: approach, avoid) × 2 (drink type: energy drink, soft drink) mixed model ANOVA investigated the effect of approach bias modification on energy drink consumption. There was no significant main effect of training condition, F(1, 108) = .58, p = .450. Unlike in Experiment 1, there was also no significant main effect of drink type, F(1, 108) = 2.36, p = .127; however, the means were in the same direction with participants consuming more of the energy drinks (M = 120 ml) than the soft drinks (M = 111 ml). Although the means indicated that participants in the avoid condition did consume slightly less of the energy drinks than participants in the approach condition, the training condition × time interaction was not statistically significant, F(1, 108) = .42, p = .518 (Table 4).

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Table 4. Mean beverage consumption in ml (SD in parentheses) for the approach and avoid conditions in Experiment 2.

https://doi.org/10.1371/journal.pone.0226387.t004

Experiment 2 Discussion

In addition to an attentional bias, the findings of Experiment 2 demonstrated the existence of an approach bias for energy drink cues (as well as soft drink cues) in regular energy drink consumers. Furthermore, approach bias modification, like attentional bias modification, eliminated the initial approach bias in the avoid group. In line with Experiment 1, approach bias modification did not have a significant effect on energy drink consumption in the taste test. However, results were in the expected direction in that energy drink intake in the avoid group was lower than in the approach group.

General discussion

The present study is the first to investigate biased decision-making processes as a possible factor in energy drink consumption. In two experiments, we demonstrated that regular energy consumers show both an attentional and approach bias for energy drink cues. We further showed that cognitive bias modification successfully reduced both biases. However, neither attentional nor approach bias modification had a significant effect on energy drink intake in a taste test.

The main focus of the current study was to examine biased decision-making processes as a possible contributing factor to energy drink consumption. Our finding that regular energy drink consumers show both an attentional and an approach bias for energy drink cues adds energy drinks to the list of substances for which cognitive biases have been shown, including alcohol, tobacco, drugs and chocolate [15–22]. The popularity of energy drinks is likely due to their high levels of caffeine. Although a handful of studies have shown an attentional bias for caffeine in coffee drinkers [23–25], none have as yet demonstrated an approach bias. Future studies will need to determine whether energy drink consumers show attentional and approach biases for energy drinks because of the addictive properties of caffeine.

Theoretically, the observed attentional and approach biases for energy drink cues are consistent with the propositions of incentive sensitisation theory [23, 24]. The pictures of energy drink cans used in the dot probe and approach-avoidance tasks likely would have provided cues with high incentive salience for our sample of regular energy drink consumers. These stimuli would therefore have been perceived as attractive and ‘wanted’. As a result, according to the theory, participants automatically directed their attention to the energy drink pictures in Experiment 1, and exhibited an automatic approach action tendency towards them in Experiment 2.

The observed approach bias here was not specific to energy drink cues, with participants also showing an approach bias for soft drink cues. These biases are, however, not necessarily mutually exclusive. First, research shows that individuals who consume energy drinks often also consume soft drinks [46]. Second, the nature of the approach-avoidance task makes it is possible for participants to demonstrate an approach bias for both the target and the control stimulus categories, as on each trial participants are presented with, and respond to, a single stimulus. By contrast, in the dot probe (attentional bias) task the target and control stimuli are paired and compared directly within trials.

Importantly, we found that the observed attentional and approach biases for energy drink cues are malleable. Attentional and approach bias modification eliminated the initial attentional and approach biases, respectively. It is noteworthy that these findings were specific to energy drink cues. Although the training procedures were conceptualised as targeting energy drinks, they inherently also target soft drinks (in the opposite direction). This demonstrates for the first time that biased processing of energy drink cues can be manipulated through targeted training, just as has been shown for other substances, in particular, alcohol, tobacco and chocolate [22, 26, 28–30].

Contrary to prediction, however, the observed reductions in attentional and approach bias were not accompanied by a corresponding lower intake of energy drinks, although the consumption pattern was in the expected direction following approach bias modification. These findings are at odds with some previous reports of reductions in alcohol and chocolate consumption following attentional [28, 31, 33] or approach [22, 30, 32] bias modification. However, many other studies have found no reduction in consumption following cognitive bias modification [34–37].

One possible explanation for the lack of cognitive bias modification effects on energy drink consumption is that a single training session, as used in the present experiments, may not have been sufficient to translate the reductions in bias into a reduction in consumption. A comparison of a single versus multiple attentional bias modification training sessions showed that although a single training session was sufficient to reduce attentional bias for chocolate, it did not reduce chocolate consumption; however, five training sessions of attentional bias modification did reduce chocolate consumption [47]. Multiple attentional bias modification training sessions have also been shown to reduce alcohol consumption [48]. Thus future research could determine whether more intensive cognitive bias modification training across multiple sessions would similarly bring about a reduction in energy drink consumption. This could be particularly the case for approach bias modification which, in contrast to attentional bias modification, did produce a slightly lower energy drink intake in the avoid group than in the approach group. In addition, a combination of both attentional and approach bias modification might be maximally effective in reducing energy drink consumption. Sharbanee and colleagues [32] showed that attentional and approach biases are distinct mechanisms that make independent contributions to alcohol consumption.

An alternative explanation for the lack of cognitive bias modification effects on energy drink consumption could be in the choice of consumption measure. Following previous studies, we used a well-established taste test protocol [31]. In our version, participants were presented with a limited selection of pre-poured energy drinks and required to taste and rate each one. This is a very different scenario from being asked to direct attention toward, or reach for, pictures of energy drink cans. Future research could use a consumption measure that more closely aligns with the attentional and/or approach bias tasks, for example, choosing a can of beverage from a vending machine. Such choice tasks are beginning to be used in the food domain [49, 50]. In addition, the taste test provides a one-off consumption measure in the laboratory. Future research could include follow-up measures of naturally occurring energy drink consumption.

Nevertheless, the present single session laboratory experiments provide proof of concept that attentional and approach biases for energy drink cues exist in regular energy drink consumers, and that these biases can be modified. In doing so, they provide a useful starting point for future clinical studies. If multi-session cognitive bias modification interventions can reduce energy drink intake, these could pave the way for randomised controlled trials in clinical samples such as heavy energy drink consumers who show signs of caffeine addiction (dependence, tolerance, withdrawal symptoms [10]) or experience negative physical and/or mental health consequences. Effective interventions are all the more important given the rising rates of energy drink consumption [1].

Furthermore, the reported frequency and amount of self-reported energy drink consumption in the present experiments puts some participants at risk of adverse physical and mental health outcomes associated with excessive energy drink intake [7–9]. These consumption habits in our Australian sample are similar to those of a Canadian sample of young adults [4]. In line with the latter study, and that of Malinauskas et al. [3] who surveyed energy drink consumption patterns in American college students a decade earlier, the main reasons reported for consuming energy drinks by the present sample were to increase energy, combat fatigue, study and drink with alcohol at parties. These reflect the concerns of young adults, who in Western societies are the core consumers of energy drinks and the target age group for the marketing of energy drinks [11].

The present study has several notable strengths. First, by focusing on both attentional and approach biases, it provided a more encompassing assessment of biased decision-making processes related to energy drink consumption. Second, the experiments used well-established tasks and protocols to assess and retrain these biases, and to measure energy drink intake. Third, the stimuli of the dot probe and approach-avoidance tasks were created to be perceptually uniform. All depicted cans of beverages in the upright position photographed against a white background. Finally, the control stimuli consisted of non-caffeinated soft drinks to provide a highly similar comparison category to energy drinks, and thus yield a robust test of attentional and approach biases for energy drink cues.

However, like most research, the current experiments are also subject to a number of limitations. First, the participants in both experiments were all regular energy drink consumers. Thus it has not been determined whether the observed attentional and approach biases are unique to energy drink consumers. Future research should include a comparison control group of non-consumers to establish the specificity of these biases in energy drink consumption. Second, the samples in both experiments consisted of undergraduate student volunteers who were not necessarily motivated to curb their energy drink intake. A recent chapter and review of cognitive bias modification pertaining to alcohol consumption concluded that effects on intake may be more reliably observed in individuals who are motivated to change their consumption behaviour [51, 52]. Thus future research should specifically recruit energy drink consumers who wish to reduce their intake. Third, as there is little variety in the everyday presentation of energy drinks (they are generally sold and served in a can), the same pictures were used for the assessment and training phases of the dot probe and approach-avoidance tasks. However, to increase generalisability, future research could test whether the observed changes in attentional and approach biases extend to untrained stimuli, as well as to different tasks assessing attentional and approach-avoidance tendencies, in line with some studies in the alcohol domain [29, 30].

In conclusion, the present experiments have contributed to the literature by demonstrating that regular energy drink consumers show attentional and approach biases for energy drink cues. They further showed that these biases can be successfully reduced via cognitive bias modification. However, such modification did not reduce energy drink consumption in the laboratory. Future research might determine whether more intensive training in samples who are motivated to reduce their energy drink intake could achieve better outcomes.

Supporting information

Acknowledgments

We are grateful to Paul Douglas for developing the software for the computerised administration of the dot probe and approach-avoidance tasks. We thank Sarah Hollitt for assistance with data collection.