The primary analysis approach was between-group differences from baseline to follow-up. The report of effects in this study focuses on effect sizes (Cohen’s d) due to the feasibility nature of the study. Several measures were normalized to account for outliers (i.e., all biological variables, AADIS, Body Mass Index, and CTQ). All statistical analyses were performed using R programming environment [79]. Descriptive statistics were obtained using the R package ‘psych’ [80] and independent samples t-tests examined group differences on demographic variables.

Linear regressions were used to evaluate relationships between baseline biological variables and ELS severity. Gender was used as a covariate in all regression analyses. For analyses involving biomarkers and genes, body mass index (BMI) was added as a covariate. Finally, for cortisol regression analyses, wake-up time and time of cortisol collection were included as covariates. To examine changes in biological variables and self-reported mental health symptoms as a function of treatment, linear mixed effects models (LMEs) were conducted using the ‘lmer’ function in R package ‘lme4’ [81] and plots were generated with ‘emmeans’ [82]. Fixed effects included group and time. Random effects included subject. Follow-up pairwise comparisons were conducted using estimated marginal means to further describe the effects of group and time on the outcome variables. Baseline MFQ scores for analysis were calculated by averaging responses across three timepoints (e.g., baseline, online assessments 1 and 2).

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