2.6. Statistical Analysis
On each study day, we evaluated sensation scores by examining the AUC, which was adjusted for baseline values with the exception of the wanting and liking measures, which were not adjusted. The calculation procedure involved the following steps: for each observation period, the area was computed by multiplying the duration (measured in minutes) of that period by the adjusted score (the absolute score minus the mean score before the meal). To derive the overall AUC (expressed as a product of score and minutes), the sum of the areas from all observation periods was determined.In each participant, the impact of the high-fat meal was calculated as the difference in the AUC on the second day (high-fat meal) minus the first day (regular meal); the effect of aversive conditioning was quantified as the difference between the third day (regular meal after previous conditioning) and the first day (regular meal preconditioning; day 3 minus day 1), and the effect of the cognitive intervention was quantified as the difference between the fourth day (postintervention) and the third day (aversive conditioning; day 4 minus day 3). For graphic representation of the data, average values and standard errors for the recorded variables within each group were calculated and are presented in the figures below.We conducted statistical analyses both within each group and between the groups. The normality of data distribution was determined using the Shapiro–Wilk test, which determined the normal distribution of data. For normally distributed data, we employed Student’s A sensitivity analysis to control for individual variations and confounders whilst obviating the need for artificial divisions between normally and nonnormally distributed data was included. The data were analysed for each day of the experiment, treatment groups were compared on the sensation score outcomes using generalized linear mixed modelling (GLMM), with time (in minutes), intervention group and time—intervention group interaction terms included. To take into account the correlation between each individual patient’s responses over time, we included the individual as a random effect. Visual inspections of residual plots were used to determine deviations from normality. In the absence of normality, robust variance estimators were calculated.The SPSS Statistics package for Windows (Version 28.0, IBM Corp., Armonk, NY, USA) was used for all calculations (significance level of 5%, two-tailed), except the generalized lineal mixed models, which were calculated using R studio version 4.1.2.All co-authors reviewed the data and provided approval for the final version of the manuscript.