Abstract
The Dual Vulnerability Model of seasonal affective disorder proposes that the cognitive-affective symptoms of seasonal depression are the result of an interaction of a diathesis of cognitive vulnerability to depression and the stressor of seasonal vegetative change. Two studies examined this hypothesis employing a within-subject design with daily data on vegetative and cognitive-affective depressive symptoms. Study 1 included a subclinical sample and a trait measure of ruminative response style. Study 2 included a clinical sample and reports of actual ruminative thoughts and behaviors in response to fatigue. Results of mixed linear model analyses in both studies supported the hypothesis that rumination moderates the relationship between the vegetative symptoms and the cognitive-affective symptoms of seasonal depression. The extension of the model to other subtypes of depression is considered.
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Notes
The effect of vegetative symptoms was modeled as a random effect, consistent with our hypothesis that their impact on cognitive-affective symptoms varies from person to person. Moderating variables terms were modeled as fixed effects. Maximum likelihood estimation was used so that the fits of the models with different fixed effects could be compared. The statistical significance of fixed effects was assessed by a t-test based on the estimate and its standard error. The statistical significance of the standard deviation of random effect variables was assessed by comparing the fit of the model to the model with the effect fixed (Pinheiro and Bates 2004).
In both studies, all dependent and independent variables were examined for being approximately normally distributed. This was the case, except for the positively skewed cognitive-affective variable in study 1, which, therefore, was log-transformed. Time-varying independent variables were person centered; those that varied only across persons were grand mean centered. In both studies visual inspection of graphs of the dependent cognitive-affective variable suggested that linear and quadratic general trends over time, both varying from person to person (i.e., as random effects), should be accounted for. In both studies, models with these terms significantly improved model fit. Next, a first-order autoregressive error structure was added. In both studies this addition also significantly improved model fit (autocorrelation ranged from .224 to .296 across all the models reported). Consequently, all subsequent models representing our hypotheses included these effects and served as the base models for assessing further improvements in model fit. For simplicity of presentation, these terms are not shown in the equation above or reported further in the Results. Details are available from the authors.
For ease of presentation, in Study 1 coefficients and standard errors have been multiplied by 1000.
Models were fit regressing rumination-to-mood on rumination-to-fatigue and vice versa, including, as before, linear and quadratic time effects and autocorrelated errors. The within-subject correlations were computed based on the reduction in variance from models without the independent variable. The value reported is the mean of the two values obtained, .4306 and .4023.
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Appendix: Factor Analysis of Visual Analogue Scale (VAS) Items
Appendix: Factor Analysis of Visual Analogue Scale (VAS) Items
Data for the factor analysis came from the 45 original participants in Study 1. Participants completed visual analogue scales (VAS; items are in Table A1) on 42–70 days (M = 51.6, SD = 7.7). This sample’s demographic characteristics were similar to those of the Study 1 subsample. First, a covariance matrix was calculated for each participant; then these matrices were averaged to yield a single covariance matrix representing the typical within-subject covariability. An exploratory principle components factor analysis with varimax rotation was conducted on the covariance matrix to assess which symptoms clustered together as they changed across time.
Between-subject factor analyses of SAD symptom data have found vegetative and cognitive-affective factors (Madden et al. 1996). In a within-subject factor analysis of data from 10 SAD patients with the same VAS variables in this study, Young and Schmitt (2000) found two similar factors, labeled energy-motivation (a subset of vegetative) and cognitive-affective, plus a doublet of the two appetite items that neither loaded on these factors nor generated a clear separate factor. Based on these results, we examined a two-factor model and excluded the two appetite items. (Models including the two appetite variables produced estimation and interpretation problems commonly found with doublet items.) This solution produced the expected energy-motivation and cognitive-affective factors. Examination of a three-factor model yielded the same cognitive-affective factor and the energy-motivation items split into a factor with the positively worded items and a factor with the negatively worded items We considered the separation of the energy-motivation items to represent method variance. We therefore adopted the two-factor, energy-motivation and cognitive-affective solution (Table A1). The energy-motivation factor accounted for 20.5% of the total variance the cognitive-affective factor accounted for 23.4% of the total variance. Six items (forgetful, difficulty making decisions, happy, trouble doing daily activities, irritable, and optimistic) did not unambiguously load on one factor.
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Young, M.A., Reardon, A. & Azam, O. Rumination and Vegetative Symptoms: A Test of the Dual Vulnerability Model of Seasonal Depression. Cogn Ther Res 32, 567–576 (2008). https://doi.org/10.1007/s10608-008-9184-z
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DOI: https://doi.org/10.1007/s10608-008-9184-z