Mindfulness and Eating Disorder Symptoms

Overview

This prospective one-arm study aims to examine short and long-term changes in dysfunctional eating among individuals who signed up for an 8-week, group based, low threshold, mindfulness-based intervention. The study is an extension of a quality assurance project which was provided by a nation-wide self-help organization for people with self-identified eating problems. Eating disorder symptoms and the proposed predictors (self-kindness, self-judgment and mindfulness) were registered online at 12 measurement time points. In the current study we will invite the same participants to complete the same measures four years after the intervention.

Recent literature-reviews suggest that mindfulness-based interventions may be effective in treating problems related to dysfunctional eating such as binge eating and emotional eating. Less is known about the processes through which change happens. Moreover, long-term follow-up is scarce. The main aim of the current project is to examine the role of self-kindness and self-judgement in predicting changes in eating disorder symptoms four years after, and to analyze longitudinal data for the entire project. Measures were administrated before the intervention, each week during the intervention, after the intervention, at follow-up 6 months after the intervention and at a second follow up four years after the intervention. The intervention is an 8-week mindfulness-based eating program (Mindful Eating Conscious Living). This intervention was administered by a nation-wide Norwegian self-help eating disorder organization. Participants who enrolled in the program (N=197) were asked to complete standardized self-report instruments at recruitment (T1), digitally during each of the 8-week sessions (T2-T9), after the intervention was completed (T10) and six months after the intervention was completed (T11). The current study - a PhD project - will invite the same participants to complete the same measures four years after completing the program. This will provide a rich data set, collected over four years. The main hypotheses are: Self-kindness and self-judgment as predictors of ED symptoms 1. During the intervention, self-kindness and self-judgment at a given week will predict eating disorder (ED) symptoms the following week (within-person). 2. Level of self-kindness and self-judgement will predict the level of ED symptoms during the whole course (pre, each week during the intervention, post and four-year follow-up) (between-person). 3\) Pre-post changes in self-kindness and self-judgment will predict level of ED symptoms at six-month and four-year follow up (between-person changes) Secondary hypotheses are: 4\) During the intervention, mindfulness (from the self-compassion scale) at a given week will predict ED symptoms the following week (within-person change) 5) Level of mindfulness will predict the level of ED symptoms during the whole course (pre, each week during the intervention, post and four-year follow-up) (between-person). 6\) Pre-post changes in mindfulness and mindful eating will predict changes in ED symptoms, and relationship and life-satisfaction, at six month and four-year follow-up (between-person changes). 7\) Pre-post changes in shame will predict changes in ED symptoms at six month and four-year follow-up (between-person change). 8\) During the intervention, expectations for positive effects of the intervention, will predict ED symptoms at the next week (within-person change). 9\) Who the instructor is, will predict pre-post changes in outcome. For each within-person hypothesis, the reverse effects will also be examined. Qualitative part of study: A randomly selected sub-group of 20 participants will be interviewed about their experiences during and after the intervention. Statistical Analysis Plan These longitudinal data will be disaggregated so that within- and between-person effects can be studied separately Repeated measurements like the present one typically has drop-outs and missing data. Therefore, we will use mixed models instead of paired t-tests, repeated measures ANOVAs, and ordinary linear regression to analyze the data. Mixed models use maximum likelihood estimation, which is the state-of-the-art approach to handle missing data (Schafer \& Graham, 2002). Especially if data are missing at random, which is likely in our study, mixed models give more unbiased results than the other analytic methods. In preliminary analyses, and for each of the dependent variables (EDE-Q, subscales form Self-compassion Scale), the combination of random effects and covariance structure of residuals that gives the best fit for the "empty" model (the model without fixed predictors except the intercept) will be chosen. Akaike's Information Criterion (AIC) will be used to compare the fit of different models. Models that give a reduction in AIC greater than 2 will be considered better (Burnham \& Anderson, 2004). The program SPSS version 28.0.1.1 (15) will be used. Possible transformations: All variables will be assessed in their original and validated format as is recommended practice, as long as this is possible with regards to statistical assumptions underlying the pre-defined analyses (i.e., multiple regression). However, if this is not possible with regards to the statistical assumptions behind the analyses, transformation (e.g., square root or log-transformations) may be needed to apply interval-based methods. The investigators will examine the degree of skewness and evaluate this against the assumptions and analyses before choosing the appropriate analysis. The pre-registered and planned analyses include multiple regression as long as assumptions are met. Alternatively, a non-parametric test will be used. Inference criteria: We pre-define the significance level: p \< 0.05 to determine significance. Missing data: Maximum likelihood

Conditions

Interventions

Eligibility

Locations (1)

Outcomes