Chi-squared tests and t-tests were used to compare demographic characteristics, EDE-Q scores and STAI-T scores between groups. Bonferroni corrections were applied to correct for multiple comparisons on subscales of the EDE-Q, with a significance level p of (0.05/4) = 0.0125.

Pattern separation and recognition memory scores were compared using an Analysis of Covariance (ANCOVA), in order to control for the effects of age, method of task delivery and antidepressant use. Age and antidepressant usage were selected as covariates due to the aforementioned findings that pattern separation abilities are known to decline over the lifespan and can theoretically be affected by the use of drugs such as selective serotonin reuptake inhibitors (SSRIs), which increase AHN [51]. Given the differences in procedure between the remote and in-person versions of the MST, method of task delivery was also included as a covariate. Relationships between variables (eating disorder psychopathology, trait anxiety, age, pattern separation and recognition memory scores) per group were assessed using Pearson product-moment correlation coefficients®.

For all analyses, effect sizes were established using Cohen’s d and interpreted as small (0.2), medium (0.5) and large (0.8) [56]. A p-value of <.05 was interpreted to warrant further investigation. Analyses were conducted in SPSS version 25 (SPSS, Inc., USA, IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.). A statistician at King’s College London was consulted during the analysis process.

Twelve participants did not contribute data to a selection of demographic and clinical variables, some of which were used as a covariate (e.g. antidepressant usage). These variables included: living status, level of education, relationship status, ethnicity, psychiatric comorbidities, medication usage, average sleep, STAI-T, EDE-Q Global Score and all subscales of the EDE-Q. The percentage of missing values varied between 11.7 and 18.4%, with a total of 298 out of 1247 records of these variables (19.3%) being incomplete. Supplementary Table 1 describes the missing data rates of each variable (see Additional file 1).

A Little’s missing completely at random test of data patterns revealed that this missingness was not completely at random (MNAR). Furthermore, separate variance t-tests revealed that missing values were significantly correlated with values of one or more variables in the dataset, indicating missingness at random (MAR). Thus, we performed multiple imputation, using the regression method in SPSS. Five imputed datasets were performed, which were pooled for analyses. All missing variables were also included in the imputation model as predictors, and age was added as an auxiliary variable.

Pooled data using multiple imputation is used in the analysis of demographic and clinical characteristics, as well as in the Pearson product-moment correlations. Due to software limitations, our main analyses could not be performed on the pooled imputed data. As such, we primarily report the ANCOVA models for complete-case analyses. For comparison, we also performed the analysis on each imputed model separately.

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