Cognitive and affective trait and state factors influencing the long-term symptom course in remitted depressed patients

<div><p>Background</p><p>Major depressive disorder (MDD) is characterized by a high risk for relapses and chronic developments. Clinical characteristics such as residual symptoms have been shown to negatively affect the long-term course of MDD. However, it is unclear so far how trait repetitive negative thinking (RNT) as well as cognitive and affective momentary states, the latter experienced during daily-life, affect the long-term course of MDD.</p><p>Method</p><p>We followed up 57 remitted depressed (rMDD) individuals six (T2) and 36 (T3) months after baseline. Clinical outcomes were time to relapse, time spent with significant symptoms as a marker of chronicity, and levels of depressive symptoms at T2 and T3. Predictors assessed at baseline included residual symptoms and trait RNT. Furthermore, momentary daily life affect and momentary rumination, and their variation over the day were assessed at baseline using ambulatory assessment (AA).</p><p>Results</p><p>In multiple models, residual symptoms and instability of daily-life affect at baseline independently predicted a faster time to relapse, while chronicity was significantly predicted by trait RNT. Multilevel models revealed that depressive symptom levels during follow-up were predicted by baseline residual symptom levels and by instability of daily-life rumination. Both instability features were linked to a higher number of anamnestic MDD episodes.</p><p>Conclusions</p><p>Our findings indicate that trait RNT, but also affective and cognitive processes during daily life impact the longer-term course of MDD. Future longitudinal research on the role of respective AA-phenotypes as potential transdiagnostic course-modifiers is warranted.</p></div

Study design.

<p>Baseline predictors. (B) Diagnostic information to define outcome variables. BDI-II BeckDepressionInventory II. MADRS Montgomery and Asberg Depression Rating Scale. SCID-I Structured Clinical Interview for DSM-IV Axis 1.</p

Estimated survival curve for remaining in remission in rMDD individuals with low and high instability of affective valence at baseline.

<p>(A) rMDD participants with low instability of affective valence (n = 28). (B) rMDD participants with high instability of affective valence (n = 28). Median split for illustrative purposes. Data from one participant were missing.</p

Simple and multiple regression models for time to relapse, chronicity, and depression scores (T2, T3) in remitted depressed patients (rMDD).

<p>Simple and multiple regression models for time to relapse, chronicity, and depression scores (T2, T3) in remitted depressed patients (rMDD).</p

Additional file 1 of Dose-dependent changes in real-life affective well-being in healthy community-based individuals with mild to moderate childhood trauma exposure

Supplementary Material

Typical residualized RT time series is shown for one patient (P029) and one condition (0-back non-jittered) for illustrative purposes.

<p>Successive RTs are connected with black lines; the outer horizontal dashed lines mark the threshold for particularly low and high RTs (Gaussian 1% threshold, cf. Methods). Very fast or slow reactions, exceeding the thresholds, are marked with circles. The red line shows the 40 s running median of values within the thresholds (background response fluctuation) and the inner horizontal dashed lines its tercile boundaries (1/3 of all values lying in each partition). The occurrence of each of these events (low/high value or omission) was associated with the tercile of the background response fluctuation. It can be seen that the few very fast RTs occur during a time of fast background RTs; similarly the larger number of very slow RTs (contributing to τ) occur preferentially during phases of slow background RT fluctuation.</p

Averages (solid lines) with 95% confidence intervals (dotted lines) are shown for controls (in black) and patients (in red).

<p>The black bar above the x-axes in figures a and b indicates an uncorrected point-wise “p<.05” difference between the groups to provide an indication where group effect sizes are considerable. “Suppression filters” (upper, thick lines in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069674#pone-0069674-g001" target="_blank">Figure 1 c-f</a>) demonstrate the impact of removing each particular frequency band from the RT time series on the group aggregate of the individual variability scores; the “extraction filters” (lower, thin lines in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069674#pone-0069674-g001" target="_blank">Figure 1 c-f</a>) show the said measures of variability for each RT time series frequency band alone. Suppression and extraction filters are therefore complementary ways of visualising one and the same relationship.</p

Additional file 3: of Bright light therapy versus physical exercise to prevent co-morbid depression and obesity in adolescents and young adults with attention-deficit / hyperactivity disorder: study protocol for a randomized controlled trial

Secondary outcome measures of the PROUD trial. Detailed description of all secondary outcome measures that will be assessed at baseline, directly after the intervention/continuing TAU (T4), and/or at 12-week follow-up (T5). (DOCX 57 kb

Additional file 1: of Bright light therapy versus physical exercise to prevent co-morbid depression and obesity in adolescents and young adults with attention-deficit / hyperactivity disorder: study protocol for a randomized controlled trial

SPIRIT Checklist. (DOC 122 kb