According to the Elaboration Likelihood Model, there are two routes to persuasion, the central route and the peripheral route. Central route processing involves in-depth and thoughtful consideration of the information presented. Information is processed via this route when participants have the ability, opportunity, resources and motivation to process information elaborately. Conversely, information processed via the peripheral route relies on the use of simple processes, such as heuristics, biases, and affective judgments, and thus requires fewer resources and less motivation. The peripheral route can be thought of, in part, as persuasion by more automatic means. With the exception of fear appeals, health promotion campaigns have typically not capitalized on peripheral route processing mechanisms and instead relied on educational approaches more reliant on central route processing. Personal relevance has emerged as an important mediator of effectiveness for these types of campaigns, presumably because it increases motivation to process information elaboratively via central pathways. This has seen a rise in computer-tailored interventions, and advocacy for behaviour change interventions to deliver tailored (individualized) or at least targeted (matched to a particular subgroup) information.  The predominant reliance on central route persuasion strategies seems to assume that persuasion via this route will be longer lasting, since attitude change that occurs via the central route should be relatively enduring and resistant to counter persuasion compared to attitudes formed based on peripheral cues. However, this ignores that people can hold automatic attitudes that may not align with their deliberative ideas, and that we are bombarded by peripheral cues that may be counter to our goals on a daily basis. Persuasion via the peripheral route may be needed to influence these more automatic attitudes (e.g., automatic evaluations), and indeed to make health topics seem relevant to the target audience in the first place. There have been several computer-based strategies developed and tested in lab-based settings to change automatic processes involved in peripheral route processing. Now with the wide-spread availability of smart-phones there are new opportunities to implement them at scale and just in time in response to contextual cues. 

Many everyday behaviours, including unhealthy dietary consumption, and sedentary behaviour, are thought to be habitual. Habit is a process whereby encountering a situation activates an impulse to act in a given way, based on learned situation-action associations. While people can inhibit their unwanted habits, often they lack the motivation or capability to do so, and so continue to act in unhealthy, habitual ways. Drawing on a fundamental distinction between habit (a process that generates impulses to act) and habitual behaviour (behaviour controlled by the habit process), this talk argues that much of the available research into how to break ‘bad’ habits has targeted habitual behaviour, rather than the habits that underlie such behaviour. For example, the ‘habit discontinuity’ approach emphasises that removing people from the environments that support their unwanted habitual actions, or capitalising on natural disruptions to such environment, can lead people to abandon old unwanted habits and adopt more desired, healthy actions. Yet, this approach leaves open the possibility that, even if the habitual behaviour has apparently been discontinued, the underlying habit may remain, and may retain its potential to automatically reactivate the unwanted action when a person is re-exposed to their old environments. This talk points to the different ways in which habit can be purposefully disrupted: habit discontinuity (discontinuing exposure to habit-facilitating environments) and habit inhibition (obstructing the translation of a habit impulse into action) target habitual behaviour, whereas habit substitution (learning a new, wanted habitual response as a direct replacement for an unwanted response) targets the underlying habit association. Although habit substitution may offer the ‘truest’ form of habit change, the pros and cons of each of these habit disruption strategies, the settings in which each might be most usefully adopted, and the techniques conducive to targeting each strategy, are set out.

Interest in targeting automatic processes such as habits and automatic evaluations is increasing as a result of the building body of evidence supporting dual process theories. Dual process theories postulate that behaviour is regulated through two forms of motivation: reflective (i.e., slow, deliberate influences that require self-regulation) and automatic (i.e., spontaneous, unintentional influences that are not reliant on self-regulation). To this point, most physical activity and nutrition interventions have targeted and assessed only reflective processes (e.g., expectations, beliefs, intentions). As a result, it remains largely unknown (1) whether automatic processes can be effectively targeted in physical activity and nutrition interventions, and (2) if changes in automatic processes lead to short- or long-term behaviour change. One major barrier to the advancement of understanding the role of automaticity in behaviour change interventions is measurement. Reflective processes are most often assessed through self-reported text-based surveys, meaning they can be easily administered to large populations, quickly and cost-effectively. With exception to self-reported habit measures, however, automatic processes are often measured implicitly. Implicit measures are indirect assessments that do not require participants’ subjective assessment. Common implicit measures of automatic processes include response-timed categorization tasks that assess implicit associations between concepts and attributes (e.g., Implicit Association Tests, priming tasks). Given recent technological advancements and wider accessibility to internet and smartphones or computers, researchers now have opportunities to measure automatic processes throughout interventions and provide essential evidence regarding automatic processes. Authors of published automaticity measurement tools have made them freely available and accessible to all researchers on open science framework repositories. I will present examples of two such measures, describe the concepts underlying their construct validity, and provide scoring data options. Now, measuring automatic processes amidst interventions is easy to administer to large populations, quickly, and cost-effectively.