In line with these insights, we have observed an increase in studies focusing on interventions to reduce risky behaviours, such as excessive drinking or eating (Allom, Mullan & Hagger, 2015; Jones, et al., 2016b, 2017; Kakoschke et al., 2017a). These studies focus on directly changing behaviour by using a cognitive training paradigm to weaken (or reverse) these cognitive biases, and/or strengthen self-control. These interventions are called ‘Cognitive Bias Modification’ (CBM) and are based on modified versions of cognitive assessment tasks (previously described, see page 9 onwards). In CBM tasks the stimulus-response contingency is manipulated (reversed) and repeated a number of times (e.g. alcohol-approach or alcohol-‘Go’ becomes alcohol-avoid or alcohol-‘No-Go’), in order to alter participants’ substance-related automatic associations, so that in the future those stimuli will evoke more appropriate responses, when they are encountered after receiving the training (Gladwin et al., 2016).
The initial development of CBM interventions started in laboratories with experiments (Allom Mullan & Hagger, 2015; Jones, et al., 2016b, 2017; Kakoschke
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et al., 2017a). These experiments aimed to test theoretical predictions and investigate causality by examining whether cognitive biases could be changed and whether these changes would result in short-lived effects on behavioural measures (such as motivation to drink, or eat, via self-reported cravings or a bogus ‘taste-test’; see Jones et al., 2016a) following a brief dose of CBM, relative to a matched control intervention. Successful results from laboratory studies provide strong justification for the evaluation of the effectiveness in clinical samples, using ideally randomized controlled trials (RCTs) of multiple sessions of CBM, compared to control (placebo- CBM) interventions, and in addition to usual treatments (for a reviews see: Allom Mullan & Hagger, 2015; Gladwin, Wiers & Wiers, 2016; Jones et al., 2016b, 2017; Kakoschke et al. 2017a).
The focus of the present thesis will be on two particular types of interventions that have found to be successful in reducing alcohol and unhealthy snacking consumption: one attempts to change motivational action tendencies (Cue Avoidance training, CAT; Wiers et al., 2011) and the other attempts to change inhibitory control (Inhibitory Control Training, ICT; Houben et al., 2012).
CAT is adapted from the AAT and participants are instructed to practice avoiding the appetitive stimuli (e.g. alcohol or chocolate) and approach neutral/control stimuli for most of the trials (90% contingency), by responding with a joystick to an irrelevant feature of the stimuli (e.g. the orientation of the stimuli: portrait versus landscape; Wiers, Rinck, Kordts, Houben, & Strack, 2010). To mask the explicit aims of the training a small number of trials (10%) are reversed.
Participants are trained to reduce their automatic approach bias towards that appetitive stimuli (e.g. alcohol) in order to consequently affect their behaviour and reduce their consumption of that substance (Wiers et al., 2013). For example, a single laboratory session of CAT was used to reverse alcohol biases and thereby reduce beer consumption in a subsequent taste-test (Wiers, Rinck, Kordts, Houben, & Strack, 2010b). More notably, these effects were extended in alcohol-dependent patients who showed a significant reduction in relapse rates in a follow-up one year later after receiving CAT compared to an active control intervention (Eberl et al., 2013; Wiers et al., 2011).
Likewise, similar effects have been reported in appetite research, in laboratory training studies which showed a reduction in unhealthy snack choices or a reduction in the consumption or craving of these after CAT (Becker, Jostmann,
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Wiers, & Holland, 2015b; Brockmeyer, Hahn, Reetz, Schmidt, & Friederich, 2015; Fishbach & Shah, 2006; Jones et al., 2017; Schumacher, Kemps, & Tiggemann, 2016). A recent review in the area, across a range of unhealthy behaviours (such as smoking, alcohol consumption and unhealthy snacking), demonstrates that CAT is an effective intervention when individuals’ approach-tendencies are successfully re- trained into avoidance, leading to reduction in consumption in both clinical and non- clinical samples, relative to controls (Kakoschke et al., 2017a).
ICT can be based on either the GNGT or the SST, and participants are instructed to practice behavioural inhibition to appetitive stimuli, by not responding to these stimuli that have been repeatedly paired to No-Go cues or to Stop signals, in order to form specific associations between specific-cues and the engagement of inhibitory control (Jones et al., 2016b, 2017). Similarly to the CAT, several studies have shown that a single session of alcohol ICT in the laboratory leads to a reduction in alcohol consumption for individuals exposed to the training, relative to controls (Houben, Havermans, Nederkoorn, & Jansen, 2012; Jones & Field, 2013).
Results have also been replicated in the eating domain, with reductions in choice and intake of unhealthy snacks (Houben & Jansen, 2011, 2015; Lawrence et al., 2015a,b; Veling, Aarts, & Stroebe, 2013a,b). For example, in a study by Veling, Aarts and Stroebe, (2013a) by pairing palatable foods with inhibition of behaviour, the consumption and evaluation of these snacks following ICT decreased. Two recent meta-analyses summarise findings from these two domains, demonstrating that ICT effectiveness of behavioural change in the laboratory is small but robust across studies (Standardized Mean Difference (SMD) = 0.43 in Jones et al., 2016b; and SMD = 0.38 in Allom, Mullan & Hagger, 2015). Unlike CAT, to date there have been no published trials that investigated the effectiveness of multiple sessions of ICT on clinical populations (e.g. alcohol-dependent patients), although evidence suggests that these effects may persist outside of the laboratory (Allom, Mullan & Hagger, 2015).
Taken together these findings on CBM seem promising. However, a great debate on the matter was aroused by a recent meta-analysis which concluded that CBM effectiveness is not robust across studies, due to the high risk of biases effecting experimental studies (Cristea, Kok, & Cuijpers, 2016). Nevertheless, experimental laboratory studies are essential in order to investigate the psychophysiological mechanisms that underlie CBM effects, before conducing
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RCTs. For this reason, results from Cristea et al.’s meta-analysis should be considered with caution as it inappropriately combines experimental laboratory studies on students with clinical and online trials.
To sum up, most research in the field concludes that CBM leads to observable behavioural changes, especially when the cognitive biases were successfully modified (for reviews see: Allom, Mullan & Hagger, 2015; Gladwin et al., 2016; Jones et al., 2016b, 2017; Kakoschke et al., 2017a). Consequently, these interventions hold great clinical potential, as cost-effective add-ons to existing treatments for risky behaviours (such as excessive drinking or eating).