Difficulties in Emotion-regulation and Interpersonal Problems During and After the COVID-19 Pandemic

Overview

Central indicators of psychological functioning such as difficulties in emotion regulation and habitual problems in one's relating to others are likely to have been substantially impacted by the COVID-19 amelioration measures of societal lock-down and physical (ne social) distancing. In turn, as these amelioration measures have been relaxed, that impact will presumably be reduced, gradually returning these factors to pre-crisis levels. Also, these factors are likely to predict mental health outcomes such as symptoms of depression and anxiety throughout the pandemic and beyond, so that levels of emotion regulation difficulties and interpersonal problems early on will predict later symptom status. Similarly reductions in such difficulties during the various phases of the outbreak will be associated with a concurrent reduction in psychological symptoms and reduced symptom levels at later stages.

Hypotheses/Research questions: H1: There will be a significant decrease in emotion-regulation difficulties (DERS) and interpersonal problems from T1 to T2. Exploratory: Examine the difference in interpersonal problems and DERS among different sub-groups before and after the COVID-19 pandemic H2: Higher level at T1 and less reduction from T1 to T2 in emotion-regulation difficulties and interpersonal problems will be associated with less reduction in anxiety and depression from T1 to T2, above and beyond, demographic variables (age, gender, education). Exploratory: Explore the different octants of IIP as predictors of changes for anxiety and depression. This study is part of 'The Norwegian COVID-19, Mental Health and Adherence Project', involving multiple studies. Statistical models: Repeated surveys like the present one typically have high drop-out and substantial 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 survey, mixed models give more unbiased results than the other analytic methods (O'Connel et al., 2017). In preliminary analyses, and for each of the dependent variables (GAD-7 and PHQ-9, DERS , IIP), 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 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 25.0 will be used (IBM Corp, 2018). First, H1 about decrease in DERS and IIP, GAD-7 and PHQ will be tested by using anxiety or depression as dependent variable in a model using time (T1 period = 0, T2 period = 1) as a predictor. Second, demographic group variables will be added as predictors. Third, the initial (T1) levels of DERS and IIP as constant covariates will be added, together with the interactions of these constant covariates with time. These interactions represent tests of H2 about the covariates predicting change in anxiety and depression. Finally, the T2 levels of DERS and IIP as constant covariates will be added, together with the interactions of these constant covariates with time. These interactions represent tests of H2 about the change in the covariates from T1 to T2 predicting change in anxiety and depression from T1 to T2s. Transformations Depending on degree of skewness compared to theoretical possibilities and interpretations, variables will be assessed in their original and validated format as is recommended practice, as long as this is possible. As this study examines psychopathology levels amongst a general population (and not a clinical population), we do expect a skewed data. We will attempt to assess these variables in their original and validated format as is recommended practice, as long as this is possible. However, if this is not possible to the statistical assumptions behind the analyses, transformation may be needed to apply interval-based methods. Alternatively a non-parametric test will be used. Inference criteria Given the large sample size in this study, we pre-define our significance level: p \< 0.01 to determine significance Missing data Maximum likelihood Sensitivity analyses Sensitivity analyses will be conducted after selecting a random sample of participants to reflect the proportion of subgroups in the Norwegian adult population. Exploratory: Questions addressed in the future paper which is not pre-specified will be defined as exploratory.