This dataset was derived from one of the investigations within a series that comprised Halim Moore's PhD thesis.

Cognitive and behavioural biases have been shown to influence dietary behaviour, particularly as a proclivity for energy-dense, palatable, and processed foods. Researchers have thus considered them a potential target for intervention to promote healthier eating behaviours. Recent advances in mobile app technology make this class of intervention more scalable and easier to adhere to than conventional dietary interventions. One such example that has demonstrated the most consistent efficacy in modifying eating behaviour to date is Response Inhibition Training. However, the efficacy and utility of Response Inhibition Training (and other types of Cognitive Bias Modification) can be considered questionable due to the heterogenous samples, implementation and outcomes selected across studies. In particular, it is unclear whether Response Inhibition Training has a similar impact on liking and wanting, and whether both outcomes in response to intervention follow a similar trend.

Participants completed both a single session of Response Inhibition Training and control mobile apps and were assessed on both liking and wanting for foods based on energy density. Trait measures of appetitive susceptibility, such as body mass index and food cue reactivity, were assessed in order to investigate whether trends in liking and wanting for these foods were congruent or divergent.

A publicly available dataset associated with a manuscript submitted to the Journal of Behavioral Medicine, "Response inhibition training as an intervention to modify liking and wanting for foods based on energy density: A proof of concept study", and Study 1 (Chapter 4) in the thesis titled "Dietary Interventions for the Real World? Addressing Hedonic Appetite and Eating Behaviours in the Digital Age".