O. Minaeva, Netherlands
University Medical Center Groningen Department of PsychiatryPresenter of 2 Presentations
EPP0519 - Overnight affective dynamics and sleep characteristics as predictors of depression and its development
ABSTRACT
Introduction
Greater affective inertia during the day (higher carry-over effects of prior affect to the current moment) is associated with depression and its development. However, the role of overnight affective inertia (from evening to morning) in depression, and the role of sleep therein, has been scarcely studied.
Objectives
We examined i) the difference in overnight inertia for positive (PA) and negative affect (NA) between individuals with past depression, current depression, and no depression; ii) how sleep duration and quality influence overnight affective inertia in these groups, and iii) whether overnight affective inertia predicts depression development.
Methods
We used data of 579 women from the East-Flanders Prospective Twin Survey. First, individuals with past (n=82), current (n=26), and no depression (n=471) at baseline were examined, and then individuals who did (n=58) and did not (n=319) develop depression at 12-months follow-up. Affect was assessed 10 times a day for 5 days. Sleep was assessed with sleep diaries. Affective inertia was operationalized as the influence of affectt-1 on affectt. Linear mixed-effect models were used to test the hypotheses.
Results
Overnight affective inertia was not associated with depression, neither was it differently associated with sleep characteristics in the depression groups. However, sleep characteristics were more negatively associated with morning NA in both depression groups compared to the non-depressed group. Overnight affective inertia did not predict the development of depression at follow-up.
Conclusions
Depression and sleep characteristics might be more related to mean affect levels rather than to more complex emotion dynamics measures. Replication of these findings with longer time-series is needed.
O110 - Screening for depression: the added value of actigraphy and smartphone-based intensive sampling of depressive affect and behaviors
ABSTRACT
Introduction
In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process.
Objectives
This study aims to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined.
Methods
We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. The performance of the models was assessed with the goodness of fit indices, calibration curves, and discriminative ability (AUC, the area under the receiver operating characteristic curve).
Results
In the development dataset, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for the ESM (AUC=0.991) and combined-domains model (AUC=0.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for the ESM (AUC=0.891) and combined-domains model (AUC=0.892).
Conclusions
ESM is a good diagnostic predictor and is easy to calculate, and, therefore, holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor, but might still be useful when active monitoring with ESM is not feasible.