'EE EMI UCL'
(AsPredicted #158,328)
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1) Have any data been collected for this study already?
No, no data have been collected for this study yet.

2) What's the main question being asked or hypothesis being tested in this study?
The primary aims of this study are to examine:
1. The impact of perceived expressed emotion (EE) on mood and behaviour in people experiencing clinically significant depressive or anxious symptomatology, i.e. a clinical sample.
2. Whether implementing an ecological momentary intervention (EMI) to support mentalisation mitigates the impact of perceived EE on mood and behaviour over time in a clinical sample compared to an active control condition.
3. Examine the acceptability and feasibility of delivering a mentalisation EMI in a clinical sample.

3) Describe the key dependent variable(s) specifying how they will be measured.
Multilevel model: mood (mood and stress) and behaviour (avoidance, assertiveness and warmth) EMA items
Pre-post: depression (PHQ-9) and anxiety (GAD-7)

4) How many and which conditions will participants be assigned to?
participants will be randomly assigned to 1 of 2 conditions
1. Mentalisation intervention
2. Mindfulness intervention (control)

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
Quantitative Analyses

Pre-post data
We will assess pre-post changes in mood (PHQ-9 and GAD-7), and secondary measures (RFQ-8 and LEE).

EMA data - Mixed-effects modelling
The data obtained from the single-case experimental phase design used in this study have a hierarchical two-level structure with observations (level 1) nested within individuals (level 2).
We will estimate a design-comparable, between base standardized mean differences (BC-SMD), using restricted maximum likelihood methods. We will model baselines including both fixed and random effects for level. The treatment phase will be modelled with linear trends with both fixed and random effects at level and slope. Assumptions were set around session level error structure (Autoregressive (AR1) with variance differing by phase).
In addition, we estimated the differences in scores between the two phases using Ruscio's A. This metric reflects the probability that a randomly selected timepoint in phase B is larger than a randomly selected timepoint in phase A (calculated via Monte-Carlo simulation: 10000 runs).

Acceptability and Feasibility
The feasibility and acceptability of undertaking a trial were demonstrated by:
• Assessment of numbers screened, number eligible and those agreeing to participate.
• Evaluating the acceptability of the intervention by assessing outcomes and retention in study.
• Evaluate the acceptability of the proposed method of data collection and data collection tools by assessing overall questionnaire response rates and for each data collection tool.
• Identify key parameters to inform the sample size calculation for the main trial.
• Evaluate the acceptability from qualitative responses around procedures (e.g. intrusiveness, ease, format)

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
For the multilevel modelling: We will exclude any participants with EMA completion rates less than 40%. A meta analysis of deviations at baseline will be run to detect outliers.
Participants with unreliable or careless responding will be excluded. This will be analysed through visual inspection of plots (floor or ceiling effects), near zero variance, and measurement error variance
Sensitivity analysis: Participants showing trends (in any direction) of magnitude 0.3 or greater in the baseline-phase will be excluded from analyses, since such trends represent change unrelated to the intervention.

7) How many observations will be collected or what will determine sample size?
No need to justify decision, but be precise about exactly how the number will be determined.
Overall, we will aim to recruit a sample of 80 participants.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)
Network Analysis
In order to inform our understanding of change over time, we also conducted a Dynamic Exploratory Graph Analysis including all ordinal EMA variables to explore the relationships between EE, mood and behaviour.