Combining motivational and volitional approaches to reducing excessive alcohol consumption in pre-drinkers: a theory-based intervention protocol
Excessive alcohol consumption is associated with increased risk of acute (e.g., accidental injury) and chronic (e.g., cardiovascular disease, cancers, diabetes, liver disease, alcohol dependence, and a range of mental health conditions) harms [1]. In Australia, national costs of excessive alcohol consumption is estimated at 15 billion dollars annually, attributed to decreased workplace productivity, strain on the healthcare system, road or vehicular accidents, crime and associated costs, illness, and death [2]. Excessive alcohol consumption is especially apparent in university populations, with a third of students drinking to hazardous levels [3, 4] and appearing to outdrink their non-student peers on drinking occasions [5, 6]. Research shows that excessive alcohol consumption significantly impairs students’ health and academic performance, and increases risk-taking behaviors such as unplanned sexual activity [7].
Recent research has focussed on pre-drinking, the practice of consuming alcohol prior to attending a subsequent event, where alcohol consumption often continues [8, 9]. Pre-drinking is also referred to as prepartying [8], pregaming [9], and pre-loading [10]. Pre-drinking has been found to constitute more than 40 % of alcohol consumption on drinking occasions [11], and an Australian multi-site study conducted in night entertainment areas found 65 % of people reported pre-drinking prior to ‘going out’ for that evening [12]. Pre-drinking has been shown to be largely socially-motivated, with pre-drinkers citing “catching up†with friends and meeting new people as precipitating factors contributing to the popularity of these sessions [13–15]. LaBrie et al. [15] found that interpersonal enhancement (i.e., pre-drinking for socialisation or enjoyment) was the strongest predictor of pre-drinking frequency and alcohol consumption, and demonstrated that pre-drinking motives differ from general alcohol consumption motives. Alcohol price has also been shown to be related to pre-drinking. Not only have students cited cost as influencing their pre-drinking [11, 16], but Miller and Droste [17] have shown that students change their hypothetical drinking decisions based on increases in the cost per drink. A recent study shows a relationship between strongly endorsing a cost motive for pre-drinking, and higher reported typical pre-drinking consumption [18].
In a series of recent studies, pre-drinking has been implicated as specifically contributing to alcohol-related harm. An event-level analysis by Barry et al. [19] found pre-drinking status significantly predicted blood-alcohol concentration, as measured by a breathalyser device. Merrill et al., [20] used event-level associations to reveal that pre-drinking on any given day was a significant predictor of alcohol related harm in university students, beyond both the total alcohol consumed on that day, and typical drinks consumed per day. In a sample of undergraduates, Caudwell and Hagger [18] found higher scores on pre-drinking cost motive items predicted higher incidence of alcohol-related harm in the previous twelve months. Pre-drinking appears to present an elevated risk to young adults, who demonstrate a lack of awareness of safe alcohol consumption limits [21], and, in laboratory settings, are unable to accurately pour a standard drink1 [22, 23]. To date, no interventions specifically aimed at reducing pre-drinking alcohol consumption have been developed. This protocol outlines a theory-based intervention that will attempt to reduce alcohol consumption during pre-drinking sessions, and the experience of alcohol-related harm.
Theory-based interventions for excessive alcohol consumption
One approach to reducing excessive alcohol consumption and alcohol-related harm among undergraduates is to develop behavioral interventions based on social psychological and motivational theories of health behavior. The use of such theories in informing interventions is important in targeting the influential determinants of health behavior, facilitating an understanding of “what works, and for whomâ€, and allows for testing of the component theories in accounting for behavior change [24]. A range of health behavioral interventions targeting excessive alcohol consumption have been developed in university student populations, incorporating brief screening and feedback [25], motivational [26–28], peer or normative feedback [29–31], planning [32], and volitional approaches [33–35]. Though the efficacy of online interventions appears to bring about small changes in alcohol consumption behaviour [d?+?= 0.14; [36]], many interventions are not theory-based, and there is evidence that theory-based interventions that closely develop intervention content to target specific psychological variables (commonly identified as correlates or predictors of alcohol consumption) are efficacious, with medium-sized effects [37, 38]. Furthermore, evidence supports the use of online delivery of alcohol interventions in student populations as they appear preferable to face-to-face methods (e.g., contact with a health professional) and may be especially useful for at-risk populations [39, 40]. Therefore, the development of a theory-based online intervention to reduce pre-drinking alcohol consumption may be a useful endeavour.
The theory of planned behavior
The theory of planned behaviour [41] has been extensively applied to predict a range of health behaviours [42–44]. The theory considers behavioural intention the focal point of behavioural engagement, where intention is formed by belief-based constructs of attitude, subjective norm, and perceived behavioral control [41]. Attitude comprises belief-based evaluations of the behavior of interest; subjective norm consists of perceived social influence regarding behavioural engagement, and; perceived behavioral control constitutes the individual’s ability to perform the behavior. The theory has been widely used across a range of health behavioural contexts, with a recent meta-analysis supporting the tenets of the theory-based model in predicting intention and behavior [44]. More recently, a meta-analysis of the theory applied to alcohol consumption behaviour has found attitudes strongly related to alcohol consumption intentions (r+?=?.62), and intentions moderately related to behaviour (r+?=?.54) with authors concluding that both attitudes and intentions towards alcohol consumption are worthwhile targets for alcohol consumption behaviour change [45]. Generally, changes in behavioral intention appear to produce small-to-moderate changes in behaviour [46], with theory-based health behavioral interventions informed by the theory of planned behavior demonstrating particular efficacy [d?+?= 0.36; [36]], supporting our advocacy of adopting a theoretical approach.
A prominent criticism of the theory is the intention-behavior gap: the relative weakness in the link between intention and behaviour [47–50]. This is an important issue for interventions where intention may be the focus, yet it is a weak or modest predictor of behavioural engagement. For example, McEachan, Conner [44] shows the intention-behaviour relationship is weaker for health risk behaviours, such as abstaining from alcohol consumption, compared to health enhancing behaviors such as diet and exercise. A recent meta-analysis investigating the relationships between the theory of planned behaviour constructs applied to alcohol consumption concluded that interventions targeting attitudes and subjective norm may be worthwhile [45]. However, there is little utility in attempting to change intention through its antecedent constructs, where a substantial intention-behaviour gap is unlikely to facilitate meaningful behaviour change. This point and the utility of the theory of planned behaviour in health behavioural research is one of current debate (see [50]), with Schwarzer [51] suggesting that post-intentional (i.e., volitional) constructs that are known to influence behaviour are of importance in interventions based on the theory of planned behaviour. Implementation intentions [52] present an approach to “closing†the intention-behavior gap by linking important contextual cues to enacting the intended behaviour in the volitional stage, increasing the likelihood that the behavior is carried out in accordance with one’s intentions.
Implementation intentions and volition
According to Gollwitzer [53], individuals who intend to reach an intended goal often fail to do so due to limitations in their ability to self-regulate behaviour. These limitations may constitute reasons such as failing to get started (e.g., forgetting or failing to act at the opportunity to do so) and getting derailed (e.g., due to attentional or competing factors; Gollwitzer Sheeran, [54]). For example, a pre-drinking goal intention may be “I intend to reduce my alcohol consumption drinking during pre-drinking sessionsâ€. However, an individual with this intention may not recognise the chance to enact that intention or fail to do so at the critical moment (e.g., where an environment is conducive to excessive alcohol consumption). Implementation intentions increase the likelihood that people will attain their intended goals by specifying contextual details of how these goals will be implemented, as well as when, and where [55]. An implementation intention for pre-drinking may therefore be “when I have finished an alcoholic drink at a pre-drinking session, I will then drink a glass of water or soft drink to help reduce my alcohol consumptionâ€. This allows individuals to switch from making conscious, effortful deliberations about enacting behaviour, to responding automatically to critical cues [52], mitigating the effects of self-regulatory limitations on carrying out intended behaviours. A meta-analysis by Gollwitzer and Sheeran [54] shows that there is a considerable effect (d
+
?=?.65) of implementation intentions in facilitating goal attainment over that of simply forming goal intentions. Importantly, implementation intention approaches have been shown to be effective in reducing alcohol consumption in young people including university students [35, 38, 56].
Key features of an implementation intention approach include detailing how the intended behaviour will be enacted. In previous studies using this approach, participants either formed their own implementation intentions [38] or chose from a menu of responses to refusing a drink with the option of developing their own plan [35]. These studies and a recent review by Hagger and Luszczynska [57] suggest that implementation intentions may be more successful if they include additional planning components that address certain contingencies in an if-then format, such as “if I am offered an alcoholic drink, then I will politely refuse by saying, ‘No thanks, I have to drive†[35]. In the context of pre-drinking, there are likely many contextual scenarios where individuals may be at risk of consuming excessive amount of alcohol (e.g., drinking games, coercion or pressure) [9, 58]. Therefore, the formation of multiple implementation intentions to address these scenarios may be especially effective in reducing pre-drinking alcohol consumption. However, compelling individuals to intend to perform certain behaviours and assisting them in doing so may not be as effective if individuals lack the necessary motivational resources to facilitate the formation of these intentions and subsequent behavior.
Self-determination theory
Another theoretical framework that has seen wide application in many health-related fields is self-determination theory [59–62]. Self-determination theory places the quality of an individuals’ motivation as influential in behavioural engagement and persistence. Individuals who exhibit controlled motivation to engage in a behaviour tend to do so because of certain external contingencies – monetary incentive or reward, or for self-esteem rationales such as avoiding guilt or blame, or embarrassment [59]. Individuals who exhibit autonomous motivation to engage in a behaviour tend to do so because it serves personally-relevant goals or the act is itself intrinsically rewarding [59]. The more autonomously motivated an individual is towards engaging in behaviour, the more likely they will be to perform and persist in performing that behaviour [63, 64]. Recent evidence indicates attitudes and intentions towards engaging in health behaviour are more strongly linked to autonomous motivation rather than controlled motivation [65, 66].
Health behavioural interventions based on self-determination focus on the facilitation of autonomous motivation [60, 67]. This is often achieved by providing autonomy support – a supportive context and rationale for the individuals’ internalising of behavioural regulation [63]. The provision of autonomy support and facilitation of autonomous motivation have demonstrated validity in engendering positive behavioural change in a wide context of health behavioural settings [62]. Within the context of alcohol consumption, studies involving self-determination theory have found relationships between autonomous forms of motivation and reductions in self-reported alcohol consumption [68], as well as intentions to keep alcohol consumption within limits, and reductions in alcohol units consumed [69]. Pavey and Sparks found that autonomy in relation to perceptions of health risk information and autonomous motivation to engage in health protective behaviours were related to participation in those behaviours [70–72].
Conversely, studies on peer influences in college drinkers have shown individuals who exhibit controlled motivation to drink excessively do so because they tend to appraise situations from a controlled orientation, related to their sense of self-esteem [73]. Therefore, an intervention that provides an autonomy-supportive context for reduced alcohol consumption may prove effective for pre-drinkers who consume alcohol excessively or in contexts where motivation to reduce excessive alcohol consumption may be lacking. Given research demonstrating the importance of autonomy in enhancing receptiveness to health risk information, and indicating intrinsic goals are more likely to be pursued than those where individuals feel compelled to pursue goals [64, 70, 72], individuals may be more autonomously motivated to reduce their pre-drinking alcohol consumption if they generate their own autonomous reasons for pursuing such a goal.
Evidence for combining approaches
A meta-analysis of internet-based health behavioral interventions has found those incorporating more behavior change techniques tended to have larger effects, potentially due to these techniques targeting different components of the behaviour change process [36]. According to the model of action phases proposed by Heckhausen and Gollwitzer [74], a “Rubicon†exists between a deliberative, or predecisional phase, and a volitional, or preactional phase. The predecisional phase incorporates the feasibility and desirability of a behavioral outcome; the motivational tendency towards enacting that behavior which leads to the formation of a goal intention [75]. The preactional phase therefore incorporates how best to meet the behavioral goal – the stage at which individuals may fall short of meeting that goal due to limitations in their ability to self-regulate behavior [75]. It follows, therefore, that interventions targeting both motivation and volitional phases of action may be more effective in evoking behaviour change.
Studies have also shown that intentions are more likely to be carried out if they are formed consistent with autonomous reasons for engaging in the target behavior [76] and when the behavior is consistent with their psychological needs [77]. Evidence shows support for a synergistic relationship between autonomous motivation and the formation of implementation intentions in facilitating goal-directed behaviour. For example, a study on goal self-concordance (i.e., the extent to which a goal-directed behaviour is self-determined), found self-concordance significantly predicted progress on a range of participant goals, and that the relationship between goal self-concordance and progress was dependent on whether or not participants formed implementation intentions [78]. Koestner et al. [79] demonstrated that participants who formed autonomy-supportive implementation intentions achieved greater goal progress than those in a neutral condition (d?=?.67). The authors attribute this to the internalisation of goals in a self-concordant manner that reflects heightened personal interest and meaning. In terms of interventions based on this premise, targeting the motivational and volitional phases in tandem show increased efficacy in reducing alcohol consumption [33], promoting exercise behavior [80], reducing saturated fat intake [81], and improving fitness [82] over either approach in isolation.
The present study
The purpose of the present study is to test an online, theory-based intervention to reduce pre-drinking alcohol consumption among undergraduate students who pre-drink. The intervention will test the effects of two theory-based techniques targeting the predecisional and implemental phases of the model of action phases through: (1) facilitating autonomous motivation to reduce pre-drinking alcohol consumption, and (2) prompting the individual to form context-specific implementation intentions to help bridge the goal intention-behavior gap. Combining these techniques should see greater reductions in pre-drinking alcohol consumption and alcohol-related harm than either approach in isolation. The current research makes an original contribution to knowledge by adopting a factorial design, which permits us to examine the independent and interactive effects of two intervention components related to different processes in the model of action phases. The research builds on previous approaches to promoting autonomous motivation [79] and based on current ‘best practice’ recommendations for using implementation intentions [57]. It also follows on from research that suggests that incorporating both motivational and implemental phases is optimally effective in changing health behaviour by targeting multiple processes [38, 80, 81].