The Efects Of Response Inhibition Training Following Binge Memory Retrieval in Young Adults Binge Eaters: A Randomised‑controlled Experimental Study Part 3

Food craving questionnaire (state). Session (1 vs 3)  ×  Time (pre vs. post cue reactivity)  ×  Group ANOVA yielded a reduction in general food craving from Session 1 to Session 3 [F(1,82)=4.058, p=0.047, η2 p=0.047], that did not significantly differ between groups. Te desire subscale showed a Session × Time × Group interaction [F(2,82)=4.273, p=0.017, η2 p=0.094]. 

Cue response refers to a specific association phenomenon produced in the brain due to a special stimulus or environmental factor. There is a strong relationship between cue response and memory. Cue responses can play a very important role in improving memory and effective learning.

If a person wants to remember something, they need to leave clues to themselves that allow them to easily recall the information in the future when they need it. Therefore, for memory improvement, establishing appropriate cue responses is very important.

Some research shows that when a person is faced with new things, they tend to associate those things with previous experiences. This connection stimulates certain areas of the brain, helping memory deepen and consolidate. If these cues are deliberately reinforced, the memory will be stronger. For example, when you learn a foreign language, if you connect the language with some related vocabulary or scenes, your memory will be deeper and stronger than simply memorizing it.

Additionally, cue responses can stimulate our creativity. When we leave clues to ourselves, we tend to connect with our existing knowledge and experiences to create new ideas and concepts. So cueing not only helps us remember things, but it also makes us smarter and more creative.

In our short life, we need to learn and experience many things. But if we fail to strengthen our cue responses, we will be haunted by things we have forgotten. Therefore, we need to hone our cue responses and use them so that we can better remember the information we need. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material with many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.

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This was found to be driven by a decrease in food desire across sessions in BMR+Sham at the post-taste-test time point [F(1,82)=7.173, p=0.007, η2 p=0.086]. Conversely, a decrease in pre-taste-test desire across sessions was seen in NR+RIT [F(1,82) = 6.498, p=0.013, η2 p=0.073]. There was a reduction in the control subscale from sessions 1 to 3 [F(1,82)=4.372, p=0.04, η2 p=0.051], which did not differ between groups. 

The same was found for the hunger subscale [F(1,82)=8.067, p=0.006, η2 p=0.09]. No significant change was observed on the relief or reinforcement subscales.

Cue reactivity. Ratings of ‘likelihood to binge on’ depicted food images (HPF vs LPF/Session 1 vs. Session 3) showed a main effect of image type (HPF>LPF: F(1,85)=564.630, p<0.0005, η2 p=0.869), validating the use of HPF and LPF images as representing binge foods and non-binge foods, respectively). A modest Group × Session interaction was observed [F(2,85)=3.931, p=0.023, η2 p=0.085]. 

While there were no group differences in Session 1, in Session 3 (post-manipulation), both BMR+RIT [t(57)=2.996, p=0.011, d=0.78] and NR+RIT [t(59)=2.74, p=0.022, d=0.71] reported lower likelihood of bingeing on any depicted food than BMR+Sham. A marginal Session  ×  Group interaction was also observed for food images’ impact on the urge to eat ratings [F(2,85)=3.167, p=0.047, η2 p=0.069]. 

BMR+RIT reported a lower ‘urge to eat’ than BMR+Sham on both sessions, however, BMR+Sham showed a lower urge to eat than NR+RIT on session 3 [t(59)=2.89, p=0.015, d=0.75]. This was largely due to an increase in desire to eat ratings in NR+RIT from session 1 to session 3 [F(1,85) = 4.037, p=0.48, η2 p=0.045].

Long-term disorder-relevant outcomes. Binge Eating Scale. The mixed modelling approach for the BES was supported by significant variance in intercepts (σ2=37.315, Z=5.519, p<0.001). A significant effect of Time (baseline (session 1), post-manipulation (session 3), 2 weeks, 3 months, 6 months, 9 months) was observed, indicating a reduction in symptom severity across the study, but no effects of Group nor interaction (see Table 3). 

Despite significant variance in the Time effect: (σ2  = 8.178, SE=3.617, Z=2.261, p=0.024), modelling Time as a random effect worsened BIC- assessed model-ft (3069.464 → 3072.779) and did not alter interpretation of any model terms. Bayes Factors (see supplement for calculation) provided substantial evidence in favour of the null hypothesis of no Group × Time effect (BF01=83). 

Bayes Factors calculated at follow-up time points for ANOVA across groups and for Welch’s t-tests contrasting RIT vs. sham (with the alternative hypothesis of lower BES scores in the RIT conditions), similarly provided evidence in favour of no differences post-intervention, but the latter were uninformative at subsequent time-points owing to reduced sample size. BF01s for these contrasts are given in Table 3.

ED symptomatology, craving and food addiction. FCQ total scores also reduced across Time from baseline to all subsequent time points [F(5,107.26)=22.719, p<0.001]. However, the Group × Time interaction was non-significant [F(10,107.351)=0.759, p=0.667]. Despite significant variance in the Time slopes (z=3.178, p=0.001), treating Time as a random effect worsened overall model ft. Similarly, negative binomial GLMM on YFAS symptom count scores showed an effect of Time [F(4,133)=2.468, p=0.048], indicating a reduction in food addiction-like symptoms from baseline to all follow-up time points (all ts≥2.066, ps≤0.039). 

The uncontrolled eating subscale of the TFEQ paralleled these effects, with significant reductions across time from Baseline to all subsequent time points [F(5,104.368)=7.663, p<0.001]. However, no signifcant Group × Time interaction was observed [F(10,104.382)=1.2, p=0.299]. Measures of disordered eating symptomatology were thus highly consistent in their pattern and supported a lack of Group effects.

Binge frequency. Poisson GLMM found a significant main effect of Time was found [F(5,84)=16.149, p<0.001]. This represented a reduction in binge episodes from baseline to all subsequent time points in all groups. No signifcant efects of Group [F(2,94)=0.028, p=0.972] or Group × Time interaction were found [F(10,85)=0.862, p=0.572]. The same pattern of results was found when modelling mean daily binge calories (using a gamma GLMM with a log link). The intervention thus had no differential impact on bingeing behaviour (Table 4). Bayes factors favoured the null in one-way ANOVA at each time point and favoured no difference, or were inconclusive, for t-tests between RIT and sham (see Table 4).

Discussion

Learned cognitive biases have been posited to be an important factor in maintaining binge eating behaviour and a prime target for therapeutic intervention. This study sought to examine the possible augmentation of the therapeutic efficacy of food response inhibition training (RIT) via putative ‘reconsolidation-update’ mechanisms in sub-clinical binge-eating young adults. 

Participants generally showed robust reductions across the spectrum of maladaptive binge behaviours assessed. However, we found very little evidence for the beneficial effects of RIT on either short-term indices of response biases (Go/No-Go and visual probe and cue reactivity), or any clinically relevant measures of eating disorder symptomatology (binge episodes, BES, YFAS). Equally, a retrieval procedure designed to elicit memory destabilisation before bias retraining produced minimal augmentation of subsequent RIT effects.

Despite evident bingeing behaviour and cognitive symptomatology, our sample displayed extremely high-performance accuracy on the Go/No-Go task, indicating relatively little in the way of premorbid response inhibition deficits to binge cues and possibly restricting the potential impact of RIT a priori. Via signal detection analysis, we observed significant, albeit modest, ‘go’ biases to all food stimuli (both HPF/binge foods and LPF/ non-binge foods) and automatic visual attentional capture by HPFs in eye-tracking metrics. We found greater reductions in response bias on the Go/No-Go task in BMR+CBM, but insufficient evidence that these differences were substantively related to eating behaviour.

While promising short-to-medium-term effects of food inhibitory control training have previously been seen in laboratory studies in ‘healthy volunteers’43,67,68 and ‘obese’ individuals (primary researchers’ terms)69, both this and recent research has observed modest effects in the majority of tested longer-term clinical endpoints and eating behaviour in disordered eating groups46,70–73. These inconsistencies may be due to different effects across (dis)ordered eating populations, focus on short-term (lab-based) vs. lasting effects and training parameters and control procedures, which are key determinates of food response inhibition train efects45.

We adopted the Go/No-Go task which effectively reduced chocolate ‘go’ bias and consumption in previous research42. It is possible that the greater diversity of high-palatability food (HPF) stimuli used here were less evocative of response biases than chocolate-only stimuli, producing more heterogeneous responses. However, the HPF stimuli in our study were individualised based on idiosyncratic ratings of the most rewarding images from a pool of food images with high normative ratings for reward value. Reactivity to these images should therefore be as high as could be expected within the bounds of an experimental setting. Eye-tracking data confirmed that HPF stimuli were salient, robustly inducing automatic visual orienting75 to a greater extent than low palatability foods, an index predictive of actual food intake76.

A more compelling explanation for the disparate findings is the inflation of previous studies’ effects by the suboptimal choice of (or lack of) control for inhibitory training procedures. Earlier studies on RIT in chocolate consumption employed a ‘control’ condition that pairs chocolate images with ‘go’ responses. This ‘go control’ is not inert, in that it may increase approach bias to chocolate images and maximise the apparent effect of RIT by artificially inflating the difference between conditions. Indeed, studies using such ‘opposing control’ conditions tend to show significant effects, whereas those using true ‘sham’ training, (50/50 Go/No-Go) do not78,79 but see Ref, an effect verified experimentally by Adams et al. 45. As ‘food → go’ training may worsen overeating symptoms, it is not a clinically viable option as a control condition. For further rationale on for the sham CBM used here, see the Supplementary Information. Despite this, some studies have found positive effects with appropriate ‘sham’ controls.

Most studies on RIT employ immediate or 24-hour post-tests and it is possible that the 1-week delay between training and the test we used prevented us from observing any immediate effects of training. However, we have observed effects in harmful drinkers from similarly brief, single post-retrieval interventions that have been evident at 1 week and at least 9 months afterwards. If RIT effects are only observable in laboratory measures and at short latencies, we must question the comparative clinical utility of such an approach.

Null findings with a specific form of CBM (RIT) here do not preclude possible effects of other CBM modalities, such as approach bias modification, which, at least within the domain of AUD, have shown more consistent clinical effects. It is possible that alternative forms of CBM would have been more effective than the RIT used in the current study. The relative efficacy of these different modalities in changing maladaptive eating behaviour remains an open question in need of assessment. However, an examination of experimental evidence published since pre-registering this study and collecting the data (https://osf.io/hjtw3) indicates that this pattern of inconsistent findings is not specific to RIT, but is reflected in the broader literature examining CBM in binge and over-eating46,71,80,81, calling us to question the key moderators and potential therapeutic impact of cognitive bias modification. 

More robust effects of CBM generally, have been found for alcohol use disorders82, suggesting effects may be reward-domain specific. Indeed, although the authors of a recent narrative review concluded favourably for CBM across reward domains78, evidence for its efficacy in modifying eating behaviour has been questioned by authors of primary studies79 who note inconsistent findings and inappropriate CBM control groups. As a whole, therefore, the field would benefit from more consistent and well-controlled task design, larger randomised controlled studies with longer-term follow-up and direct assessment of the relative efficacy of different CBM modalities across different reward domains.

Limitations.

This study aimed to assess whether RIT efficacy could be catalysed by conducting retraining following the ‘reactivation’ of maladaptive food reward memory, as we have shown for behavioural and drug interventions48,50. We did not find evidence of such effects, aside from in short-lived Go/No-Go task performance interpreting this as a reconsolidation-update effect would be tenuous. However, demonstrating therapeutic enhancement via maladaptive memory reminders is dependent upon a memory-targeting intervention having a minimum of standalone efficacy. Since CBM was largely ineffective, even in the short-term (in-lab) measures of responding collected here, we are unable to make any conclusions as to whether food reward memories successfully destabilised our retrieval procedure nor whether this could confer additive benefit in longer-term clinical outcomes to a standalone behavioural therapy for binge eating. Multiple sessions of training could be used, although one of the great appeals of a putative reconsolidation-based therapy is its single-shot nature.

Binge-eating individuals frequently already engage in compensatory strategies to regulate their weight and minimise binge episodes, including effortful inhibition of food approach, and avoidance of ‘trigger’ foods. Our eye-tracking data support this notion. The complex relationship that binge eating individuals have with binge foods thus entails reward and approach, but also avoidance, self-criticism and shame84 following bingeing. If binge-eating individuals are already well-practised in trigger food avoidance strategies, the potential for added efficacy of brief avoidance training may have been limited a priori. Identifying target sub-groups with high levels of baseline response bias may yield greater effects of retraining. While we have found minimal evidence in the current study to recommend RIT as a clinical intervention in binge eating, given the relative ease of its implementation (e.g. via smartphone apps), limited potential for harm when constructed correctly and potential to orient more attention to one’s eating behaviours and related cognitions, there may be a rationale to recommend pursuing RIT approaches in these groups.

Our study sample was not receiving treatment and binge eating behaviour was primarily assessed via self-report instruments, which may be considered sub-optimal. However, it was not our intention to diagnose binge eating in this study and we focussed on adolescent sub-clinical binge eaters as a group in whom preventative, low-intensity interventions might be usefully employed. The existence of the relevant disordered eating behaviours was further triangulated against other disordered eating measures and food logs. Regarding these, one reviewer noted that MyFitnessPal usage is prevalent among disordered eating populations85 and on pro-eating disorder forums, questioning the ethics of its use in the current setting. While there is no current evidence for a causal link between MyFitnessPal usage and eating disorders and the reductions in eating disorder symptomatology across all groups in the current study suggest it was not a cause of harm, future studies may instead wish to use recovery-focussed apps, such as Recovery Record.

Strengths.

We employed a highly rigorous randomised, pre-registered design including more appropriate control procedures than some previous studies, and a comprehensive assessment of both short-term target cognitive processes and long-term eating behaviour and disorder symptomatology, with a follow-up period considerably longer than prior research. This allows us to fairly comprehensively reject the possibility of lasting intervention efficacy over a clinically relevant timeframe. Doing so, we found no evidence of a lasting beneficial effect of inhibitory control training, either alone or when combined with pre-training maladaptive memory retrieval.

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