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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161504/ https://www.ncbi.nlm.nih.gov/pubmed/34054657 http://dx.doi.org/10.3389/fpsyg.2021.653610 |
Sumario: | 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. |
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