The Efects Of Response Inhibition Training Following Binge Memory Retrieval in Young Adults Binge Eaters: A Randomised‑controlled Experimental Study Part 2

HPF Cue reactivity and ‘taste test’. The procedure is outlined in detail in the Supplementary Information. Briefly, ‘pleasantness’, ‘desire to eat’, and ‘likelihood of bingeing on’ was assessed for 18 HPF and 18 LPF images on a 0–100 scale. From this task, individualized HPF and LPF images (four of each) were selected per participant, for later use in the visual probe and Go/No-Go tasks based on the highest and lowest reward reactivity ratings. 

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Before image rating, participants selected a preferred HPF snack food item from a ‘menu’ and were told they would eat this after rating some food images, in a sham ‘taste test’. The selected food was placed in front of the participant and visible during the ratings of all food images and at the end, the picture rating was itself rated for ‘desire to eat’ and predicted ‘enjoyment’ pre-consumption and its taste attributes, true ‘enjoyment’ and ‘wanting more’, post-consumption. The food was consumed according to on-screen prompts requiring participants to ‘pick up food’, ‘prepare to eat’, and ‘eat the food.

Go/No-Go Task. Response bias to binge foods was both assessed and retrained via a Go/No-Go task, adapted from Houben and Jansen42 and following previous research 38,62. Full task details are given in the Supplementary Information and Ref.63. 

An ‘assessment version’ of the task was used in Sessions 1 and 3 and a ‘modification version’ in Session 2 (‘intervention’ session). Task parameters were identical in both versions except HPF binge foods were paired with ‘No-go’ responses and LPF images paired with ‘Go’ responses on 100% trials in the ‘modification’ version. The ‘sham’ version of the Go/No-Go task in session 2 was simply the ‘assessment’ version; with parity between requirements for Go- or No-go responses for all stimulus types (HPF binge food, LPF, or filler). Assessed indices of response bias were error rates, median reaction times, sensitivity (d-prime) response bias (criterion C), and indexing bias to ‘go’ to images regardless of response requirement42.

Visual probe. Eye-tracking in a dot-probe task was used to assess attentional bias to the self-selected LPF and HPF stimuli. All food images were paired with matched non-food images and dwell time and first fixation latency were calculated as indices of sustained and automatic attention, respectively. Details in Supplementary Information.

Binge memory retrieval and no-retrieval control. Participants in the BMR+RIT and BMR+sham groups underwent Binge Memory Retrieval (BMR) which followed a procedure parallel to those we have used successfully in previous studies on maladaptive reward memory reconsolidation48,64. The BMR procedure was introduced to the participants as a repeat of the session one ‘taste test’ (i.e. cue reactivity) task. Again, participants selected their favorite food from the ‘menu’ and were instructed that they would consume this after rating images. 

The presented images were the participant’s four highest-rated ‘binge cues’. They then rated their predicted enjoyment and ‘desire to eat’ their selected food. Following this, the on-screen consumption prompts read as before. The final prompt, however, read ‘Stop, put food down’ at which point the food was taken away. Participants were thus prevented from consuming their anticipated food reward, putatively engendering a cognitive prediction error.

Participants in the NR condition followed the same procedure as BMR, except (1) the binge food cues were replaced with the lowest-rated LPF food images from the cue reactivity task and (2) Instead of selecting their favorite HPF from the menu, participants were given a non-binge LPF (celery sticks) and told they would eat this afer rating food images. 

Thereafter, the image and food ratings and prompt screens were identical to the BMR procedure, including the prediction error procedure. The NR procedure was designed to match the BMR as closely as possible without (re)activating binge food reward memory.

Procedure.

After screening, participants attended three lab sessions and (remotely) provided follow-up data on four additional occasions (+2 weeks, 3 months, 6 months, and 9 months). Before lab sessions, they fasted from solid food (4 h) and abstained from caffeine (2 h). All lab sessions were conducted between 1 and 5 p.m. Written informed consent was given at the start of Session 1, following eligibility screening. The full procedure is outlined in detail in the Supplementary Information.

Session 1. Baseline demographic, questionnaire, biological (including blood glucose, blood pressure, weight & height for BMI calculation), and eating-related measures were obtained (see supplement for full list). In addition, state measures of food craving (FCQ) and hunger (hunger ruler) were assessed followed by the cue reactivity procedure and the assessment version of the Go/No-Go task. Finally, they completed the visual probe task.

Session 2 (session 1+48 h). After repeating the biological and state measures from session 1, participants then completed the BMR or NR procedure as appropriate to their random group allocation. As with our previous studies48,49, following the BMR or NR procedure, participants completed high-load working memory tasks (prose recall from the Rivermead battery and digit span forwards and backward), to ensure cognitive disengagement from the food cues. Following completion of these ‘distractor’ tasks (~5 min), participants began the ‘RIT’ or ‘sham’ version of the Go/No-Go task, followed by FCQ-state and ‘hunger ruler’.

Session 3 (session 2+7 days). The Session 3 procedure was identical to Session 1, except the participants did not complete the BIS, BIS/BAS, or BDI scale.

Follow-up. At 2 weeks, 3, 6, and 9 months following Session 3, participants remotely completed the BES, EDEQ, Y-FAS, TLFB of binges, TFEQ, and PFS and rated each image used in the initial cue reactivity assessment task on the same metrics as in-lab.

Statistical approach. In-lab continuous measures (cue reactivity rating data, Go/No-Go reaction times, oculomotor attentional bias, and state questionnaire measures) were assessed with 2 [Session: Session 1 (manipulation) v. Session 3 (post-manipulation))×3 [BMR+RIT, BMR+sham, NR+RIT]×mixed ANOVA. Power calculation was based on this model (see Supplementary Information for full data handling protocols, sample size calculation data, and randomization). For analysis of cue reactivity and Go/No-Go RT data, a factor of Cue Type (HPF, LPF, non-food filler) was also modeled. 

For error rate and accuracy data in the Go/No-Go task, generalized estimating equations with a log-linear link function were used due to the approximate Poisson distribution of the count data. For long-term follow-up data, linear mixed models (LMMs; for continuous, normally distributed data) and generalized linear mixed models (GLMMs; binge count data) were used, incorporating effects of Group, Timepoint (baseline, post-manipulation, 2 weeks, 3 months, 6 months and 9 months) and their interaction. 

Signal detection metrics criterion C (i.e. ‘g bias’ and d ′ were calculated for the Go/No-Go task) and analyzed with LMMs and gamma GLMM (following inspection of data distribution). For tests of baseline trait, biometric, and demographics variables, where group differences were not hypothesized, the false-discovery rate (FDR65) adjusted alpha level was applied. Post-hoc tests following omnibus tests were adjusted using the Sidak correction. Data were collected by LS and EC and analyzed blind by RKD, using a code generated by SKK.

Ethical approval.

The authors assert that all procedures contributing to this work were approved by and comply with the University College London Research Ethics Committee’s ethical standards on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. ISRCTN Registration Identifier: ISRCTN13262256. Open Science Framework Pre-registration: https://osf.io/82c4r/.

Results

Descriptive statistics for key variables across groups are given in Table 1. Groups were very similar on assessed demographic variables, being typically in their early 20s and higher education. BES scores verified subjective binge-eating status and the PFS, TFEQ, and FCQ indicated relatively high reactivity to food, emotional/uncontrolled eating, and food craving indicating the sample displayed robust maladaptive reward responses to food. There was a trend for greater BMI in BMR+RIT than the other groups, due to three individuals with particularly high BMI (~37). There was also a trend for a difference (BMR+Sham>BMR+RIT) in the uncontrolled eating subscale of the TFEQ. Neither of these differences approached significance at FDR-corrected alpha. Groups were otherwise similar on baseline variables.

Short‑term effects of RIT and BMR (in‑lab measures).

Pre-manipulation, and Go/No-Go task commission errors (False Alarms) were greater for both types of food stimuli (LPF and binge) than non-food stimuli. See Supplementary Information for full analyses. Error rates were examined across Groups, Sessions (pre-manipulation vs post-manipulation), Stimulus Types (Binge, LPF, non-food filler), and Error Types (misses and false alarms). In line with the analysis of baseline data, the main effects of Stimulus Type (χ2 (2)=82.194, p<0.001), Error Type (false alarms>misses): χ2 (1)=6.404, p=0.011 and their interaction (χ2 (2)=13.013, p=0.001) were found. 

The four-way interaction of Group, Stimulus Type, Error Type, and Session was also significant. A three-way Stimulus Type × Session × Error Type interaction was present in all groups, although simple effects within each Group showed a change in response to Binge food stimuli only in BMR+RIT (see Table 2, top). At baseline, BMR+RIT showed significantly more false alarms than misses to binge food images (χ2 (1)=18.043, p<0.001), however, this was abolished post-training (χ2 (1)=1.222, p=0.269).

To qualify this effect, Session × Stimulus Type interactions were assessed within each Group and Error Type (see Table 2, bottom). This showed a significant increase in binge-food ‘misses’ from session 1 to session 3 in BMR+RIT, but a significant decrease in misses (i.e. greater response to binge food) in BMR+Sham, indicating a potential worsening of approach bias in this group. In NR+RIT, there was a significant decrease in false alarms on binge food ‘no-go’ trials and a decrease in false alarms to filler images.

Signal detection measures. Criterion C. A 3 (Group)×2 (Session: pre-manipulation, post-manipulation) × Stimulus Type (Binge, LPF, fller) factorial linear mixed model with bootstrapped parameter estimates found main effects of Stimulus Type [F(2,450)=3.59, p=0.028] and a Group  ×  Session  ×  Stimulus Type interaction [F(4,450)=3.011, p=0.018]. The 3-way interaction was investigated through examination of Session × Group interactions for each Stimulus Type. This revealed a Session*Group interaction for binge images only.

In BMR+Sham, there was a significant worsening of response bias to food, reflected in a reduction in C for binge images from session 1 to session 3 [F(1,90)=6.14,p=0.015]. In BMR+RIT, there was a significant reduction in bias to binge images (increase in C towards 0) [F(1,90)=4.635, p=0.034]. In NR+RIT there was no statistically significant change [F(1,90) = 3.153,p = 0.079]. This is possibly evidence of a beneficial response in BMR + RIT, although it should be noted that this group showed the greatest bias to binge images on Session 1, indicating potential baseline dependency effects. This effect is shown in Fig. 1.

D prime (d′). As with overall accuracy, d′ scores were highly skewed (z>4 in most cases), indicating ceiling-level performance about going/no-go signal sensitivity. For this reason, d′ scores were analyzed using a gamma generalized linear mixed model, including factors of Group, Stimulus Type, and Session factorially. This yielded a main effect of Stimulus Type only [F(2,522)=4.124, p=0.016], indicating lower d′ scores (reflecting greater false alarm rate) to binge food images vs. non-food filler images [t(522)=2.783, p=0.017], but no difference between HPF and LPF stimuli [t(522)=0.766, p=0.444].

Reaction time data. At baseline, median reaction times on (correct) ‘Go’ trials indicated an effect of Stimulus Type [F(2,174) =8.447, p<0.001, η2 p=0.089], that was invariant across groups [Stimulus Type × Group interaction: F(4,174) = 1.948, p=0.105, η2 p=0.043]. Responses were faster to both types of food images (HPF and LPF) than non-food fller images [Helmert F(1,87)=14.82, p<0.001, η2 p=0.146], but not different between HPF and LPF images [Helmert F(1,87)=0.089, p=0.766, η2 p=0.001]. Tus there was an overall faster response to food images in the study sample, but not specifically to HPF ‘binge’ foods. A general speeding of responses between sessions 1 and 3 indicated practice effects [F(1,87)=32.643, p<0.001, η2 p=0.273], but there were no interactions nor group effects.

Oculomotor attentional bias (visual probe). Dwell time assessment of HPF/binge food vs LPF food images found no evidence for differential sustained attention to binge-food images above any food image per se [main effect of image type F(1,85)=2.79, p=0.099, η2 p=0.032]. Equally, this did not vary pre-post manipulation [Session × Image Type F(1,85)=0.7, p=0.792, η2 p=0.001] or across Groups [Session × Image Type × Group F(2,85)=0.43, p=0.65, η2 p=0.01]. Dwell time on long-latency trials (2000 ms) incorporates early automatic and later conscious control of visual attention. 

Indeed, significantly reduced latencies to first fixation on binge food images were observed (a measure of automatic attentional capture) vs LPF images [main effect of image type [F(1,85)=27.508, p<0.001, η2 p=0.245. Combined with the lack of difference in dwell time, this suggested that following initial (automatic) attentional capture, participants deployed effortful visual avoidance strategies to disengage attention from binge food images.

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