Early Warning Signals Based on Momentary Affect Dynamics can Expose Nearby Transitions in Depression: A Confirmatory Single-Subject Time-Series Study

Abstract

Background: In complex systems early warning signals such as rising autocorrelation, variance and network connectivity are hypothesized to anticipate relevant shifts in a system. For direct evidence hereof in depression, designs are needed in which early warning signals and symptom transitions are prospectively assessed within an individual. Therefore, this study aimed to detect personalized early warning signals preceding the occurrence of a major symptom transition.

Methods: Six single-subject time-series studies were conducted, collecting frequent observations of momentary affective states during a time-period when participants were at increased risk of a symptom transition. Momentary affect states were reported three times a day over three to six months (95-183 days). Depressive symptoms were measured weekly using the Symptom CheckList-90. Presence of sudden symptom transitions was assessed using change point analysis. Early warning signals were analysed using moving window techniques.

Results: As change point analysis revealed a significant and sudden symptom transition in one participant in the studied period, early warning signals were examined in this person. Autocorrelation (r=0·51; p<2.2e^-16), and variance (r=0·53; p<2.2e^-16) in 'feeling down', and network connectivity (r=0·42; p<2.2e^-16) significantly increased a month before this transition occurred. These early warnings also preceded the rise in absolute levels of 'feeling down' and the participant's personal indication of risk for transition.

Conclusions: This study replicated the findings of a previous study and confirmed the presence of rising early warning signals a month before the symptom transition occurred. Results show the potential of early warning signals to improve personalized risk assessment in the field of psychiatry.

Keywords: complex systems; depression; early warning signals; experience sampling methodology; momentary mental states; networks; time-series analyses.

Figure 1

Early warning signals precede symptom transition and absolute changes in “feeling down” Note. The upper panel shows the changes in weekly depressive symptoms measured with the SCL-90 depression subscale, and in levels of “feeling down” measured three times a day with experience sampling. The middle panel shows the moving window estimates of the autocorrelation of “feeling down”. The lower panel shows the moving window estimates of the total internode network connectivity. Note that the moving window procedure is the reason the x-axis starts at assessment 90.

Figure B.1

shows the raw scores of ‘feeling down’ over time (blue dots) combined with the trend line resulting from the kernel-weighted local polynomial smoothing (brown line). The deviations between the observed values and the smooth trend were used for further analyses.

Figure B.2

illustrates the changes in weekly depressive symptoms as measured with the SCL-90 depression subscale, and in levels of “feeling down” measured three times a day with experience sampling (upper panel). We rescaled the “feeling down” score using a division by 2 to plot the measures in the same graph. The lower panel shows the moving window estimates of the variance of “feeling down”. Note that the moving window procedure is the reason why the time axis starts at assessment 90. The middle and lower panel show the moving window estimates, respectively, of the autocorrelation and variance when calculated based on the total sum of all momentary mental states.

Figure B.3

The lower panel shows the moving window estimates of the variance of “feeling down”. Variance peaks before absolute levels of “feeling down” start to increase.

Figure B.4

shows the change in internode instrength separately for feeling “stressed”, “not energetic”, “irritable”, “tired” and “down”. Y-axes are the moving window estimates of instrength for each affect state. Autocorrelation was not included in the calculation of instrength. Feeling irritable, tired, and down showed a clear rise in instrength before the symptom transition occurred. Feeling not energetic showed a modest increase. Feeling stressed, however, showed a rise in instrength in the period after the transition. These findings may serve to generate new hypotheses on the microlevel processes involved in the development and maintenance of symptoms.

Figure B.5

Autocorrelation and network results for the other participants in the pilot data with usable time-series data who did not experience a significant symptom transition. We rescaled the autocorrelation score, by multiplying with a factor 50, to plot these values in the same graphs with the absolute value of feeling down. These patterns are depicted, per participant, in the upper panel. The lower panel shows the change over time in internode network connectivity. The x-axis represents time in terms of measurement moments.