Development of Momentary Appetite Capture (MAC): A versatile tool for monitoring appetite over long periods.

Understanding how an intervention impacts appetite in real-life settings and over several days remains a challenging and under-explored research question. To this end, we developed Momentary Appetite Capture (MAC), a form of ecological momentary assessment that combines automated text messaging with an online platform. Participants report their appetite using visual analogue scales (hunger, desire to eat, and fullness) and a virtual portion-size selection task. In two separate studies, we assessed the feasibility and test-retest reliability of MAC. Participants were prompted every 2 hours over a 14-hour window, and they repeated this assessment over two consecutive weekdays. For each participant, we calculated a daily time-averaged area under the curve (AUC) for each appetite measure. In Study One (N = 25) time-averaged AUC was significantly positively correlated across test days for hunger (r = 0.563, p = .003), desire to eat (r = 0.515, p = .008) and prospective portion size (r = 0.914, p < .001), but not for fullness (r = 0.342, p = .094). Participants completed 95% of MACs (380 of 400), and we used participant feedback to improve the MAC tool and study protocol for Study Two. In Study Two (N = 31), 94% of MACs were completed (468 of 496). Across days, time-averaged AUC was significantly positively correlated for hunger (r = 0.595, p = < .001), fullness (r = 0.501, p = .004), desire to eat (r = 0.585, p < .001), and prospective portion size (r = 0.757, p < .001). Together, these studies suggest that MAC could be an acceptable and reliable tool to track appetite throughout the day. In the future, MAC could be used to explore the impact of weight-loss interventions on natural fluctuations in appetite.

"It helps me to stay on the right path, rather than give in": Mixed-method process evaluation of the ImpulsePal app-based intervention for weight management.

Background: Empirical research indicates that impulsive processes that operate below conscious monitoring can undermine peoples' attempts to change behaviour patterns, especially those that have become habitual. This may, therefore, be a serious challenge for those trying to lose weight. A novel smartphone app-based intervention (ImpulsePal) offers practical strategies to manage impulsive urges to facilitate reductions in the consumption of energy-dense processed food and overeating. Aim: This process evaluation of ImpulsePal aimed to explore what was delivered/received and used, mechanisms of action, and potential contextual factors impacting intervention engagement and outcomes. Methods: A mixed-methods process evaluation, with composite analysis of the quantitative (app usage statistics) and qualitative data (semi-structured interviews), was conducted alongside a feasibility randomised controlled trial with individuals with a body mass index of at least 25 kg/m2 who wanted to lose weight. Results: Of 58 participants receiving ImpulsePal, 56 had successfully shared app usage statistics, and 36 (62%) were interviewed. Although usage statistics indicated reductions in the use of some features, interviews indicated that participants were still using app-recommended strategies without requiring them to open ImpulsPal. Overall, interviews highlighted that participants valued having access to in-the-moment support, felt more aware of their own eating behaviour and influences on it, and felt an increased ability to avoid and reconceptualise, in-the-moment temptations. Conclusion: This process evaluation offers support for a logic model suggesting that impulse management, using ImpulsePal, can promote healthier eating among those motivated to lose weight. It also highlights the necessity of using multimodal methods to explore the delivery and use of digital interventions.

The Effect of Intermittent Fasting on Appetite: A Systematic Review and Meta-Analysis.

Previously, narrative reviews have considered the effects of intermittent fasting on appetite. One suggestion is that intermittent fasting attenuates an increase in appetite that typically accompanies weight loss. Here, we conducted the first systematic review and meta-analysis to quantify the effects of intermittent fasting on appetite, when compared to a continuous energy restriction intervention. Five electronic databases and trial registers were searched in February 2021 and February 2022. Abstracts (N = 2800) were screened and 17 randomized controlled trials (RCTs), consisting of a variety of intermittent fasting regimes, met our inclusion criteria. The total number of participants allocated to interventions was 1111 and all RCTs were judged as having either some concerns or a high risk of bias (Cochrane RoB 2.0 tool). Random effects meta-analyses were conducted on change-from-baseline appetite ratings. There was no clear evidence that intermittent fasting affected hunger (WMD = -3.03; 95% CI [-8.13, 2.08]; p = 0.25; N = 13), fullness (WMD = 3.11; 95% CI [-1.46, 7.69]; p = 0.18; N = 10), desire to eat (WMD = -3.89; 95% CI [-12.62, 4.83]; p = 0.38; N = 6), or prospective food consumption (WMD = -2.82; 95% CI [-3.87, 9.03]; p = 0.43; N = 5), differently to continuous energy restriction interventions. Our results suggest that intermittent fasting does not mitigate an increase in our drive to eat that is often associated with continuous energy restriction.

An App-Based Intervention for Pediatric Weight Management: Pre-Post Acceptability and Feasibility Trial.

BACKGROUND: A multidisciplinary approach to weight management is offered at tier 3 pediatric weight management services in the United Kingdom. Encouraging dietary change is a major aim, with patients meeting with dieticians, endocrinologists, psychologists, nurse specialists, and social workers on average every other month. OBJECTIVE: This research sought to trial an inhibitory control training smartphone app-FoodT-with the clinic population of a pediatric weight management service. FoodT has shown positive impacts on food choice in adult users, with resulting weight loss. It was hoped that when delivered as an adjunctive treatment alongside the extensive social, medical, psychological, and dietetic interventions already offered at the clinic, the introduction of inhibitory control training may offer patients another tool that supports eating choice. In this feasibility trial, recruitment, retention, and app use were the primary outcomes. An extensive battery of measures was included to test the feasibility and acceptability of these measures for future powered trials. METHODS: FoodT was offered to pediatric patients and their parents during a routine clinic appointment, and patients were asked to use the app at home every day for the first week and once per week for the rest of the month. Feasibility and acceptability were measured in terms of recruitment, engagement with the app, and retention to the trial. A battery of psychometric tests was given before and after app use to assess the acceptability of collecting data on changes to food choices and experiences that would inform future trial work. RESULTS: A total of 12 children and 10 parents consented (22/62, 35% of those approached). Further, 1 child and no parents achieved the recommended training schedule. No participants completed the posttrial measures. The reasons for not wanting to be recruited to the trial included participants not considering their weight to be connected to eating choices and not feeling that the app suited their needs. No reasons are known for noncompletion. CONCLUSIONS: It is unclear whether the intervention itself or the research processes, including the battery of measures, prevented completion. It is therefore difficult to make any decisions as to the value that the app has within this setting. Important lessons have been learned from this research that have potential broad relevance, including the importance of co-designing interventions with service users and avoiding deterring people from early-stage participation in extensive data collection.

The relationship between meat disgust and meat avoidance-A chicken-and-egg problem.

Feelings of disgust toward meat have been researched for at least 30 years, but so far the causal relationship that may link meat disgust and meat consumption has remained elusive. Two possible pathways have been proposed in previous literature: the more common pathway seems to be that meat disgust is developed after a transition to vegetarianism, potentially via the process of moralization and recruitment of (moral) disgust. Other accounts suggest the existence of a second pathway in which disgust initiates the avoidance of meat and this can be explained by existing theories of disgust functioning as a pathogen avoidance mechanism and meat serving as a pathogen cue. However, the evidence base for either relationship remains thin and to our knowledge no research has examined whether temporary meat abstention can lead to increases in meat disgust, as the first pathway suggests. We measured meat disgust and meat intake in n = 40 meat eaters before and after attempting a meat-free diet for 1 month (while taking part in the annual vegan campaign Veganuary). Although most participants lapsed to eating meat during this period, we found that reductions in meat intake during the month were predictive of increases in meat disgust afterwards. This supports the view that meat disgust is expressed as a result of meat avoidance in meat eaters. Implications for theoretical understanding of the relationship between meat disgust and meat avoidance, as well as the development of disgust based interventions are discussed.

The impact of COVID-19 on the eating habits of families engaged in a healthy eating pilot trial: a thematic analysis.

BACKGROUND: The eating habits of children and adults have been impacted by the COVID-19 pandemic, with evidence of increases in snacking and emotional eating, including eating to relieve boredom. We explored the experiences of families with children aged 4-8 years who had recently participated in a healthy eating pilot trial when the first national lockdown began in England. METHODS: Eleven mothers were interviewed in April and May 2020. Interview questions were developed based on the COM-B model of behaviour. Four main themes were constructed using inductive thematic analysis. RESULTS: The first theme related to an initial panic phase, in which having enough food was the primary concern. The second related to ongoing challenges during the lockdown, with sub-themes including difficulties accessing food, managing children's food requests and balancing home and work responsibilities. The perception that energy-dense foods met families' needs during this time led to increased purchasing of (and thus exposure to) energy-dense foods. In the third theme, families described a turning point, with a desire to eat a healthier diet than they had in the early stages of the lockdown. Finally, in the fourth theme, families reported a number of strategies for adapting and encouraging a balanced diet with their children. CONCLUSIONS: Our results suggest that even if parents have the capability (e.g. knowledge) and motivation to provide a healthy diet for their family, opportunity challenges (e.g. time, access to resources, environmental stressors) mean this is not always practical. Healthy eating interventions should not assume parents lack motivation and should be sensitive to the context within which parents make feeding decisions.

ImpulsePal: The systematic development of a smartphone app to manage food temptations using intervention mapping.

BACKGROUND: Impulsive processes driving eating behaviour can often undermine peoples' attempts to change their behaviour, lose weight and maintain weight loss. AIM: To develop an impulse management intervention to support weight loss in adults. METHODS: Intervention Mapping (IM) was used to systematically develop the "ImpulsePal" intervention. The development involved: (1) a needs assessment including a qualitative study, Patient and Public advisory group and expert group consultations, and a systematic review of impulse management techniques; (2) specification of performance objectives, determinants, and change objectives; (3) selection of intervention strategies (mapping of change techniques to the determinants of change); (4) creation of programme materials; (5) specification of adoption and implementation plans; (6) devising an evaluation plan. RESULTS: Application of the IM Protocol resulted in a smartphone app that could support reductions in unhealthy (energy dense) food consumption, overeating, and alcoholic and sugary drink consumption. ImpulsePal includes inhibition training, mindfulness techniques, implementation intentions (if-then planning), visuospatial loading, use of physical activity for craving management, and context-specific reminders. An "Emergency Button" was also included to provide access to in-the-moment support when temptation is strong. CONCLUSIONS: ImpulsePal is a novel, theory- and evidence-informed, person-centred app that aims to support impulse management for healthier eating. Intervention Mapping facilitated the incorporation of app components that are practical operationalisations of change techniques targeting our specific change objectives and their associated theoretical determinants. Using IM enabled transparency and provided a clear framework for evaluation, and enhances replicability and the potential of the intervention to accomplish the desired outcome of facilitating weight loss through dietary change.

A Randomised Controlled Trial of Inhibitory Control Training for Smoking Cessation: Outcomes, Mediators and Methodological Considerations.

Objective: Inhibitory control training (ICT) has shown promise for improving health behaviours, however, less is known about its mediators of effectiveness. The current paper reports whether ICT reduces smoking-related outcomes such as craving and nicotine dependence, increases motivation to quit and whether reductions in smoking or craving are mediated by response inhibition or a devaluation of smoking stimuli. Method: Adult smokers (minimum 10 cigarettes per day; N = 107, M age = 46.15 years, 57 female) were randomly allocated to receive 14 days of smoking-specific ICT (named INST; a go/no-go task where participants were trained to not respond to smoking stimuli) or active control training (participants inhibited responding toward neutral stimuli). Participants were followed up to 3-months post-intervention. This trial was preregistered (Australian and New Zealand Clinical Trials Registry ID: ACTRN12617000252314; URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370204). Results: There were no significant differences between ICT and active control training groups. Specifically, participants in both groups showed significant reductions in craving, nicotine dependence, motivation and a devaluation (reduced evaluation) of smoking-stimuli up to 3-months follow-up compared to baseline. Inhibition and devaluation of smoking stimuli did not act as mediators. Devaluation of smoking stimuli was an independent predictor of smoking and craving at follow-up. Conclusion: Inhibitory control training (ICT) was no more effective at reducing smoking-related outcomes compared to the active control group, however, significant improvements in craving, dependence indicators and evaluation of smoking stimuli were observed across both groups. A return to basic experimental research may be required to understand the most effective ICT approach to support smoking cessation.

Effects of go/no-go training on food-related action tendencies, liking and choice.

Inhibitory control training effects on behaviour (e.g. 'healthier' food choices) can be driven by changes in affective evaluations of trained stimuli, and theoretical models indicate that changes in action tendencies may be a complementary mechanism. In this preregistered study, we investigated the effects of food-specific go/no-go training on action tendencies, liking and impulsive choices in healthy participants. In the training task, energy-dense foods were assigned to one of three conditions: 100% inhibition (no-go), 0% inhibition (go) or 50% inhibition (control). Automatic action tendencies and liking were measured pre- and post-training for each condition. We found that training did not lead to changes in approach bias towards trained foods (go and no-go relative to control), but we warrant caution in interpreting this finding as there are important limitations to consider for the employed approach-avoidance task. There was only anecdotal evidence for an effect on food liking, but there was evidence for contingency learning during training, and participants were on average less likely to choose a no-go food compared to a control food after training. We discuss these findings from both a methodological and theoretical standpoint and propose that the mechanisms of action behind training effects be investigated further.

Food-related inhibitory control training reduces food liking but not snacking frequency or weight in a large healthy adult sample.

Inhibitory control training has recently been used as an intervention to aid healthy eating and encourage weight loss. The aim of this pre-registered study was to explore the effects of training on food liking, food consumption and weight loss in a large (n = 366), predominantly healthy-weight sample. Participants received four training sessions within a week, in which they had to inhibit their responses to either energy-dense foods (active group) or non-food images (control group). Subjective food ratings, food consumption frequency and weight were measured pre- and post-training. At two-weeks post-training, the active group reported a greater reduction in liking for energy-dense foods, compared to the control group. Active participants also reported a significantly greater increase in healthy food liking, immediately post-training, relative to the control group. There was no statistically significant difference between groups for the change in consumption of trained foods or for weight loss. These findings are partially consistent with previous research conducted in smaller, more overweight samples. Exploratory analyses suggest that some effects of training may be driven by awareness effects. Methodological differences across findings and avenues for future investigation are discussed.

Exploring Strategies to Optimise the Impact of Food-Specific Inhibition Training on Children's Food Choices.

Food-specific inhibition training (FSIT) is a computerised task requiring response inhibition to energy-dense foods within a reaction-time game. Previous work indicates that FSIT can increase the number of healthy foods (relative to energy-dense foods) children choose, and decrease calories consumed from sweets and chocolate. Across two studies, we explored the impact of FSIT variations (e.g., different response signals, different delivery modes) on children's food choices within a time-limited hypothetical food-choice task. In Study 1, we varied the FSIT Go/No-Go signals to be emotive (happy vs. sad faces) or neutral (green vs. red signs). One-hundred-and-fifty-seven children were randomly allocated to emotive-FSIT, neutral-FSIT, or a non-food control task. Children participated in groups of 4-15. No significant FSIT effects were observed on food choices (all values of p > 0.160). Healthy-food choices decreased over time regardless of condition (p < 0.050). The non-significant effects could be explained by lower accuracy on energy-dense No-Go trials than in previous studies, possibly due to distraction in the group-testing environment. In Study 2, we compared computer-based FSIT (using emotive signals) and app-based FSIT (using neutral signals) against a non-food control with a different sample of 206 children, but this time children worked one-on-one with the experimenter. Children's accuracy on energy-dense No-Go trials was higher in this study. Children in the FSIT-computer group chose significantly more healthy foods at post-training (M = 2.78, SE = 0.16) compared to the control group (M = 2.02, SE = 0.16, p = 0.001). The FSIT-app group did not differ from either of the other two groups (M = 2.42, SE = 0.16, both comparisons p > 0.050). Healthy choices decreased over time in the control group (p = 0.001) but did not change in the two FSIT groups (both p > 0.300) supporting previous evidence that FSIT may have a beneficial effect on children's food choices. Ensuring that children perform FSIT with high accuracy (e.g., by using FSIT in quiet environments and avoiding group-testing) may be important for impacts on food choices though. Future research should continue to explore methods of optimising FSIT as a healthy-eating intervention for children.

Meat disgust is negatively associated with meat intake - Evidence from a cross-sectional and longitudinal study.

Meat consumption is increasingly seen as unsustainable, unhealthy, and unethical. Understanding what factors help people reduce their meat intake is urgently needed. One such factor is meat disgust, a feeling reported by many vegetarians, and which could be a promising basis for meat reduction interventions. However, meat disgust and its impact on meat consumption is poorly understood. We examined meat disgust and its role in vegetarianism and reducing meat intake in a cross-sectional and longitudinal online study. We measured self-reported meat consumption, meat disgust (by self-report and Implicit Association Test), meat liking, self-control, and disgust sensitivity in N = 711 adults (57% omnivores, 28% flexitarians, 15% vegetarians) recruited from a community cohort. Results showed that 73% of vegetarians can be classified as 'meat disgusted', and that meat disgust predicted meat intake better than self-control in omnivores and flexitarians at baseline. Following up a sub-sample of participants (N = 197) after six months revealed that changes in meat intake over time were also associated with changes in meat disgust. This is the first study to quantify the impact of meat disgust on (changes in) meat consumption and its prevalence in the vegetarian and the general population. Our findings advance research into meat disgust and encourage the development of disgust-based interventions to reduce meat intake.

App-based food Go/No-Go training: User engagement and dietary intake in an opportunistic observational study.

Food Go/No-Go training aims to alter implicit food biases by creating associations between perceiving unhealthy foods and withholding a dominant response. Asking participants to repeatedly inhibit an impulse to approach unhealthy foods can decrease unhealthy food intake in laboratory settings. Less is known about how people engage with app-based Go/No-Go training in real-world settings and how this might relate to dietary outcomes. This pragmatic observational study investigated associations between the number of completed app-based food Go/No-Go training trials and changes in food intake (Food Frequency Questionnaire; FFQ) for different healthy and unhealthy food categories from baseline to one-month follow-up. In total, 1234 participants (m(BMI) = 29 kg/m2, m(age) = 43years, 69% female) downloaded the FoodT app and completed food-Go/No-Go training at their own discretion (mean number of completed sessions = 10.7, sd = 10.3, range: 1-122). In pre-registered analyses, random-intercept linear models predicting intake of different foods, and controlled for baseline consumption, BMI, age, sex, smoking, metabolic syndrome, and dieting status, revealed small, significant associations between the number of completed training trials and reductions in unhealthy food intake (b = -0.0005, CI95= [-0.0007;-0.0003]) and increases in healthy food intake (b = 0.0003, CI95 = [0.0000; 0.0006]). These relationships varied by food category, and exploratory analyses suggest that more temporally spaced training was associated with greater changes in dietary intake. Taken together, these results imply a positive association between the amount of training completed and beneficial changes in food intake. However, the results of this pragmatic study should be interpreted cautiously, as self-selection biases, motivation and other engagement-related factors that could underlie these associations were not accounted for. Experimental research is needed to rule out these possible confounds and establish causal dose-response relationships between patterns of engagement with food Go/No-Go training and changes in dietary intake.

When do children learn how to select a portion size?

The reduction of portion sizes supports weight-loss. This study looks at whether children have a conceptual understanding of portion size, by studying their ability to manually serve a portion size that corresponds to what they eat. In a clinical setting, discussion around portion size is subjective thus a computerised portion size tool is also trialled, with the portion sizes chosen on the screen being compared to amounts served manually. Children (n = 76) age 5-6, 7-8 and 10-11 were asked to rate their hunger (VAS scale), liking (VAS scale) and 'ideal portion size for lunch' of eight interactive meal images using a computerised portion size tool. Children then manually self-served and consumed a portion of pasta. Plates were weighed to allow for the calculation of calories served and eaten. A positive correlation was found between manually served food portions and the amount eaten (r = 0.53, 95%CI [0.34, 0.82, P < .001), indicating that many children were able to anticipate their likely food intake prior to meal onset. A regression model demonstrates that age contributes to 9.4% of the variance in portion size accuracy (t(68) = -2.3, p = .02). There was no relationship between portion size and either hunger or liking. The portion sizes chosen on the computer at lunchtime correlated to the amount manually served overall (r = .34, 95%CI [0.07, 0.55], p < .01), but not in 5-6-year-old children. Manual portion-size selection can be observed in five-year olds and from age seven, children's 'virtual' responses correlate with their manual portion selections. The application of the computerised portion-size tool requires further development but offers considerable potential.

No evidence that prefrontal HD-tDCS influences cue-induced food craving.

This study investigated whether the application of high definition transcranial DC stimulation (HD-tDCS) to the dorsolateral prefrontal cortex reduces cue-induced food craving when combined with food-specific inhibitory control training. Using a within-subjects design, participants (N = 55) received both active and sham HD-tDCS across 2 sessions while completing a Go/No-Go task in which foods were either associated with response inhibition or response execution. Food craving was measured pre and post stimulation using a standardized questionnaire as well as desire to eat ratings for foods associated with both response inhibition and response execution in the training task. Results revealed no effect of HD-tDCS on reducing state food craving or desire to eat. Due to the COVID-19 pandemic, we were unable to achieve our maximum preplanned sample size or our minimum desired Bayesian evidence strength across all a priori hypotheses; however 6 of the 7 hypotheses converged with moderate or stronger evidence in favor of the null hypothesis over the alternative hypothesis. We discuss the importance of individual differences and provide recommendations for future studies with an emphasis on the importance of cognitive interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Cognitive and environmental interventions to encourage healthy eating: evidence-based recommendations for public health policy.

Policymakers are focused on reducing the public health burden of obesity. The UK average percentage of adults classified as obese is 26%, which is double that of the global average. Over a third of UK adults report using at least one weight management aid. Yet, many people still struggle to change their diet-related behaviour, despite having the awareness, intention and capability to do so. This 'intention-behaviour gap' may be because most existing dietary-choice interventions focus on individual decision-making, ignoring the effects of environmental cues on human behaviour. Behaviour change interventions that 'nudge' people into making healthier choices by modifying the food environment have been shown to be effective. However, this type of intervention is typically challenging for policymakers to implement for economic, ethical and public accessibility reasons. To overcome these concerns, policymakers should consider 'boosting' interventions. Boosting involves enhancing competences that help people make decisions consistent with their goals. Here, we outline cognitive training as a boosting intervention to tackle obesity. We synthesize the evidence for one type of cognitive training (go/no-go training) that may be effective at modifying food-related decisions and reducing body weight. We offer evidence-based recommendations for an obesity-focused Public Health Wales behaviour change programme.

Food Addiction: Implications for the Diagnosis and Treatment of Overeating.

With the obesity epidemic being largely attributed to overeating, much research has been aimed at understanding the psychological causes of overeating and using this knowledge to develop targeted interventions. Here, we review this literature under a model of food addiction and present evidence according to the fifth edition of the Diagnostic and Statistical Manual (DSM-5) criteria for substance use disorders. We review several innovative treatments related to a food addiction model ranging from cognitive intervention tasks to neuromodulation techniques. We conclude that there is evidence to suggest that, for some individuals, food can induce addictive-type behaviours similar to those seen with other addictive substances. However, with several DSM-5 criteria having limited application to overeating, the term 'food addiction' is likely to apply only in a minority of cases. Nevertheless, research investigating the underlying psychological causes of overeating within the context of food addiction has led to some novel and potentially effective interventions. Understanding the similarities and differences between the addictive characteristics of food and illicit substances should prove fruitful in further developing these interventions.

Do restrained eaters show increased BMI, food craving and disinhibited eating? A comparison of the Restraint Scale and the Restrained Eating scale of the Dutch Eating Behaviour Questionnaire.

Despite being used interchangeably, different measures of restrained eating have been associated with different dietary behaviours. These differences have impeded replicability across the restraint literature and have made it difficult for researchers to interpret results and use the most appropriate measure for their research. Across a total sample of 1731 participants, this study compared the Restraint Scale (RS), and its subscales, to the Dutch Eating Behaviour Questionnaire (DEBQ) across several traits related to overeating. The aim was to explore potential differences between these two questionnaires so that we could help to identify the most suitable measure as a prescreening tool for eating-related interventions. Results revealed that although the two measures are highly correlated with one another (rs = 0.73-0.79), the RS was more strongly associated with external (rs = -0.07 to 0.11 versus -0.18 to -0.01) and disinhibited eating (rs = 0.46 versus 0.31), food craving (rs = 0.12-0.27 versus 0.02-0.13 and 0.22 versus -0.06) and body mass index (rs = 0.25-0.34 versus -0.13 to 0.15). The results suggest that, compared to the DEBQ, the RS is a more appropriate measure for identifying individuals who struggle the most to control their food intake.

A randomized controlled trial of inhibitory control training for smoking cessation and reduction.

OBJECTIVE: The high rates of illness and mortality associated with cigarette smoking necessitate the development of novel reduction and cessation treatments. Inhibitory control training (ICT) has recently emerged as a potentially efficacious intervention to reduce the consumption of alcohol and unhealthy food. This randomized controlled trial was the first to investigate the effect of Internet-delivered ICT on cigarette consumption in a community sample of heavy smokers. METHOD: For the present study, 107 adult smokers (mean age = 46.15 years; 57 female) who smoked a minimum of 10 cigarettes per day and met criteria for a moderate or severe tobacco use disorder were recruited. Participants were randomly allocated to receive go/no-go training in which either smoking stimuli (intervention) or nonsmoking stimuli (control) were paired with no-go signals and were instructed to complete 1 training session per day over a 2-week period. This trial was preregistered with the Australian and New Zealand Clinical Trials Registry (Trial ID: ACTRN12617000252314). RESULTS: We found no significant differences between conditions on percentage of days abstinent or daily cigarette consumption, although there was a significant decrease in daily cigarette consumption across both conditions. Further, we found no significant moderating effects of impulsivity on the relationship between cigarette consumption and the 2 tasks. CONCLUSIONS: Although participants in both conditions reduced their daily cigarette consumption, the intervention task was no more successful than the control task was in achieving cigarette abstinence or reduction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Giving pork the chop: Response inhibition training to reduce meat intake.

Meat consumption is damaging to the environment, health and animal welfare. Despite a growing interest in reducing meat intake, many people eat too much. This is partly due to the pleasure associated with eating meat. Research has used go/no-go response inhibition training (RIT) to reduce the intake and reward value (liking) of snack foods. However, RIT has not yet been applied to meat. We investigated whether an internet-delivered RIT with meat pictures would reduce meat intake and liking relative to a non-food control RIT condition. Participants (N = 81) were meat eaters with a desire to reduce their intake. They completed four 10-min training sessions in one week. Active participants inhibited responses to meat, and responded to fruits and vegetables. Meat intake was measured using food frequency questionnaires at pre- and one month post-training, and a daily meat diary for one week during training. Liking of meat and other foods was measured at pre- and one month post-training. There was a reduction in meat intake over one month in both groups, with active participants showing a significantly larger decrease than controls. Conditions did not differ in meat intake during the training week. Both groups showed a devaluation of meat, with active participants showing a significantly larger devaluation of food overall, suggesting some generalisation of devaluation effects. The reduced frequency of meat intake was associated with greater devaluation of meat in active but not control participants. These findings suggest that meat RIT helps to reduce meat intake and food liking. Future research should conduct larger randomised controlled trials with longer-term outcomes.