A novel feature of this study is the inclusion of the IRAP72 (Barnes-Holmes et al., 2006; Barnes-(Barnes-Holmes, Barnes-(Barnes-Holmes, Stewart & Boles, 2010), which is a promising implicit measurement approach. The IRAP is a computer-based task which requires participants to respond to a set of stimuli which is either consistent or inconsistent with their learning history (Nicholson, McCourt
& Barnes-Holmes, 2013). The main premise of the IRAP is that respondents give quicker responses when the stimulus and response fit with their beliefs (e.g., “chocolate cake comforts me when feeling stressed”). The IRAP is theoretically underpinned by RFT (Hayes, Barnes-Holmes & Roche, 2001)73.
Emotional eating is typically measured by direct self-report (explicit) measures. In contrast to implicit measurement, currently self-report measures remain the gold standard in assessment for emotional eating (Bongers et al., 2013). However, explicit measurement makes a number of problematic assumptions: (a) cognitions are available to introspection, and that (b) self-reports accurately reflect these underlying cognitions (Greenwald & Banaji, 1995). There is considerable evidence to suggest that – whether due to a lack of awareness or due to secondary cognitions that moderate reporting (e.g., sensitivity to social expectations or social desirability bias) – self-report may be unreliable. The IRAP has the potential benefits associated with implicit forms of assessment. Unlike self-report, implicit measures are not as vulnerable to demand characteristics (Dawson, Barnes-Holmes, Gresswell, Hart & Gore, 2009). This is particularly important for a socially sensitive concept like emotional eating which may evoke shame and be perceived as a stigma in a society striving for thinness (Fazio & Olson, 2003; Annis, Cash & Hrabosky,
72 Information relating to the implicit measure is focused on only in the extended paper.
73 See section 1.6.1 for additional information on RFT to avoid duplication.
166 2004; Puhl & Heuer, 2009). The IRAP has demonstrated its efficacy by
measuring behaviours and targeting cognitions not picked up by self-report (McKenna, Barnes-Holmes, Barnes-Holmes & Stewart, 2007). Evidence for this is based on research showing divergent responses to implicit versus explicit measures of the same construct (Power, Barnes-Holmes, Barnes-Holmes &
Stewart, 2009).
Furthermore, the IRAP is a comprehensive measure capturing implicit cognition, allowing the exploration of secondary appraisal rather than mere association (Golijani-Moghaddam, Hart & Dawson, 2013). The IRAP is also theoretically consistent with ACT as opposed to other forms of implicit
measurement, and so it is a useful means of assessing whether the intervention changes the targeted variables. Within the mainstream cognitive literature, implicit measures are characterised as measuring relatively fast/automatic responses which may occur with reduced awareness or control (e.g., Gawronski
& Payne, 2010). Importantly, responses on implicit measures have been shown to predict overt behaviour (McConnell & Leibold, 2001) and potential markers of clinical change (Teachman & Woody, 2003). Implicit responses offer important information given their ability to make predictions about clinically relevant behaviour (Hussey, Thompson, McEnteggart, Holmes & Barnes-Holmes, 2015).
The IRAP was selected because it is considered more advanced in comparison to standard implicit measures, such as the Implicit Association Test (IAT, Greenwald, McGhee & Schwartz, 1998) which focuses on associations (Nicholson et al., 2013; Hughes, Barnes-Holmes & De Houwer, 2011). The IRAP is considered to have better content validity in comparison to the IAT as it can assess propositional relations (Golijani-Moghaddam et al., 2013). In
comparison to the IAT, the IRAP is considered less susceptible to faking
(McKenna et al., 2007). Furthermore, the IRAP has been considered beneficial because it can predict behavioural outcomes (Dawson et al. 2009). Research studies in particular have demonstrated that implicit beliefs assessed via the IRAP impact on behaviour more so than beliefs assessed by explicit measures (Dawson et al., 2009; Nicholson & Barnes-Holmes, 2012).
Although implicit measures capture different information in comparison to their explicit counterparts, it is highlighted that they do not
167 completely separate automatic and controlled processes (De Houwer, Teige-Mocigemba, Spruyt & Moors, 2009). The Relational Elaboration and Coherence model (REC), which is underpinned by RFT is one explanation for what both types of measures assess (Barnes-Holmes et al., 2010). From a REC model perspective, it is hypothesised that (a) implicit and explicit responses are based at opposite ends of a continuum, rather than being discrete separate processes;
(b) implicit measures assess immediate responding, whereas explicit measures assess extended relational processes; and (c) convergence occurs when
responses on both implicit and explicit measures are similar (i.e., immediate responses are in line with extended responses), whereas when they are different they are considered as divergent.
Currently, there are no studies in the literature which have used the IRAP specifically in relation to emotional eating, nor as part of a SCED
methodology. However, a study examining the effectiveness of an intervention, using the IRAP exists. Hooper, Villatte, Neofotistou, and McHugh, (2010) compared acceptance (mindfulness) and avoidance (thought suppression) approaches to negative emotions. The authors found that the acceptance-based approach led to a change in IRAP responding. However, this study focused on negative responses in particular and there was no focus on emotional eating.
In the remit of emotional eating, explicit measures may be criticised for mainly focusing on negative emotions (which is an oversight considering that individuals may engage in emotional eating in response to positive emotions as well; Nolan, Halperin & Geliebter, 2007). Using an implicit measure may be a way around this. Bongers and colleagues (2013) explored the IAT as a measure of emotional eating, which included positive, negative and neutral words and pictures of food. Participants in this study were then shown a film to assess the impact on their mood before being presented with food. The authors
hypothesised that emotional eaters would eat more in the control condition in comparison to those who were not. They found that those who scored higher on emotional eating in response to both positive and negative emotions ate more during the positive mood induction and consumed more than non-emotional eaters. Furthermore, a similar study conducted by Goldstein and colleagues (2014), explored implicit and explicit attitudes to disinhibited eating of chocolate
168 consumption. They found that a discrepancy between attitudes predicted the amount of chocolate consumed, moderated by impulsivity.
Using implicit measures as an adjunct to explicit measures is useful.
Although the efficacy of implicit measures have not been fully established, they are still useful in helping increase our understanding allowing comparisons between both forms of measurement. This is also important because of the issues associated with self-report measures for emotional eating, such as recall bias as individuals may struggle to recall emotions and their eating behaviour, as well as the association between the two (Bongers et al., 2013; Hassan, 2005). Therefore, the measurement of emotional eating suffers from a “triple recall bias” (Evers et al., 2011).