Dual process models point towards an interaction between topdown control and bottom-up influences. Research suggests obesity is linked to low self-control combined with high reward sensitivity. Childhood obesity is linked to low inhibitory control and greater activation of neural reward centres in response to food cues. Higher inhibitory control is linked to greater consumption of fruit and vegetables among children. Can interventions help develop inhibitory control capacity?
New approaches to healthy eating are looking at targeting the brain s response to food via a computer game instead of telling people what to do. Inhibitory control training (ICT): Go/No-Go task Healthy foods = Go Unhealthy foods = No-Go Successful inhibition required Decreases intake Increases healthy choice, decreases unhealthy choice And even leads to weight loss in adults
One study found an effect on sweets and chocolate intake (non-food control task). 7 6 5 4 p =.117 p =.008 p =.554 Studies run by Exeter 3 undergraduates 2 investigated effects on food choice and produced 1 inconsistent results (50/50 food control task). 0 Study 1 Study 2 Study 3 Active Control
To measure changes in food choice, a pretraining choice task was added. To compare across studies, a non-food control task was included. To check hypothetical vs. real measures, a real food choice was added. Active Food Control Non-Food Control
Phase 1: Random allocation Hypothetical food task Hunger scale Break (5-10 days) Phase 2: Go/No-Go training Hypothetical food task Hunger scale Real food choice
Proportion of HF chosen of total choices N = 81; 45 male (A = 29; FC = 25; NFC = 27) ANCOVA controlling for pre-training choices as a covariate 0.6 0.5 0.4 0.3 Average proportion of Healthy Foods Chosen Pre Post Outcome expressed as a proportion due to pretraining errors 0.2 0.1 0 Active Food Control Non-food
Proportion of HF chosen of total choices N = 81; 45 male (A = 29; FC = 25; NFC = 27) Pre-training HF choice (covariate) = F 1,77 = 60.77, p <.001 0.6 0.5 0.4 0.3 Average proportion of Healthy Foods Chosen Pre Post Condition = F 2,77 = 5.17, p =.008 0.2 A vs. FC: p =.012 A vs. NFC: p =.005 FC vs. NFC: p =.784 0.1 0 Active Food Control Non-food
Proportion of HF chosen of total choices N = 81; 45 male (A = 29; FC = 25; NFC = 27) Change from pre- to posttraining: 0.6 0.5 0.4 Average proportion of Healthy Foods Chosen Pre Post A = t 28 = -4.79, p <.001, Cohen s d z =.89, JSZ B = 496.1 FC = p =.477, d z =.14, JSZ B = 3.74 0.3 0.2 0.1 NFC = p=.353, d z =.18, JSZ B = 3.27 0 Active Food Control Non-food
Real food choice N = 69; 38 male (A = 25; FC = 21; NFC = 23) 1.4 1.2 Average number of Healthy Foods Chosen Real vs. Card Choices correlated well = r s =.661, p <.001 1 0.8 0.6 Condition on choice (Kruskal-Wallis) = x 2 2 = 4.76, p =.047; (one-sided test) 0.4 0.2 0 Active Column1 Food Control Non-Food Control
Real food choice N = 69; 38 male (A = 25; FC = 21; NFC = 23) 1.4 1.2 Average number of Healthy Foods Chosen Planned comparisons (Mann-Whitney) A vs. NF: U = 188.5, p =.028 1 0.8 0.6 0.4 All other comparisons: p >.204 0.2 0 Active Column1 Food Control Non-Food Control
Participants in the active group chose the greatest number of healthy foods. This was due to an increase in healthy food choice from pre- to posttraining. 50/50 food response task may not be appropriate for control groups.
No measure of absolute liking for foods (e.g., was it a decrease in unhealthy or an increase in healthy?) No change data for real food task. No follow-up data to test longevity of effects.
Does 50/50 food response training lead to change in real food measures? What are the mechanisms of such training? Can training effects withstand the effects of advertising on intake? How does it work? STOP Automatic inhibition? Stimulus devaluation? Both? Advertising is particularly problematic when it comes to children s energy-dense food intake. Can inhibitory control training compete?
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