Individual Negative Affective Trajectories Can Be Detected during Different Depressive Relapse Prevention Strategies

An investigation of the potential clinical utility of critical slowing down as an early warning sign for recurrence of depression

BACKGROUND AND OBJECTIVES

Much of the burden of depressive illness is due to relapses that occur after treatment into remission. Prediction of an individual's imminent depressive relapse could lead to just-in-time interventions to prevent relapse, reducing depression's substantial burden of disability, costs, and suicide risk. Increasingly strong relationships in the form of autocorrelations between depressive symptoms, a signal of a phenomenon described as critical slowing down (CSD), have been proposed as a means of predicting relapse.

METHODS

In the current study, four participants in remission from depression, one of whom relapsed, responded to daily smartphone surveys with depression symptoms. We used p-technique factor analysis to identify depression factors from over 100 survey responses. We then tested for the presence of CSD using time-varying vector autoregression and detrended fluctuation analysis.

RESULTS

We found evidence that CSD provided an early warning sign for depression in the participant who relapsed, but we also detected false positive indications of CSD in participants who did not relapse. Results from time-varying vector autoregression and detrended fluctuation analysis were not in agreement.

LIMITATIONS

Limitations include use of secondary data and a small number of participants with daily responding to a subset of depression symptoms.

CONCLUSIONS

CSD provides a compelling framework for predicting depressive relapse and future research should focus on improving detection of early warning signs reliably. Improving early detection methods for depression is clinically significant, as it would allow for the development of just-in-time interventions.

The Duration-Adjusted Reliable Change Index: Defining Clinically Relevant Symptom Changes of Varying Durations

The time period over which relevant symptoms shifts unfold is not uniform across individuals. This article proposes an adaptation of the Reliable Change Index (RCI) to detect symptom changes of varying durations in individual patients' time series: the Duration-Adjusted RCI (DARCI). The DARCI proportionally raises the RCI cut-off to account for its extension over additional time increments, resulting in different DARCI thresholds for different change durations. The method is illustrated with a simulation study of depressive symptom time series with varying degrees of discontinuity and overall mean change, and four empirical case examples from two clinical samples. The results suggest that the DARCI may be particularly useful for identifying symptom shifts that appear relatively abrupt, which can help indicate when a patient is showing significant improvement or deterioration. Its ease of use makes it suitable for application in clinical contexts and a promising method for exploring transitions in psychiatric populations.

Psychopathological networks: Theory, methods and practice

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Explaining the efficacy of an internet-based behavioral activation intervention for major depression: A mechanistic study of a randomized-controlled trial

Background

Behavioral activation is an effective treatment for depression that is theorized to facilitate structured increases in enjoyable activities that increase opportunities for contact with positive reinforcement; to date, however, only few mechanistic studies focused on a standalone intervention.

Method

Interventions using internet-based behavioral activation or psychoeducation were compared based on data from a randomized-controlled trial of 313 patients with major depressive disorder. Activation level and depression were measured fortnightly (baseline, Weeks 2, 4, 6, 8, 10), using the Patient Health Questionnaire-9 and the Behavioral Activation for Depression Scale-Short Form, respectively. Analysis was performed to determine if a change in activation level mediated treatment efficacy.

Results

Latent growth modeling showed that internet-based behavioral activation treatment significantly reduced depressive symptoms from baseline to the end of treatment (standardized coefficient = −.13, p = .017) by increasing the rate of growth in the activation level (mediated effect estimate = −.17, 95% CI [−.27, −.07]. Results from mixed effects and simplex models showed that it took 4 weeks before mediation occurred (i.e., a significant change in activation that led to a reduction in depressive symptoms).

Conclusion

Activation level likely mediated the therapeutic effect of behavioral activation on depression in our intervention. This finding may be of significant value to clinicians and depressed individuals who should anticipate a 4-week window before seeing a prominent change in activation level and a 6-week window before depressive symptomatology reduces. Future research must consolidate our findings on how behavioral activation works and when mediation occurs.

Tapering Antidepressants While Receiving Digital Preventive Cognitive Therapy During Pregnancy: An Experience Sampling Methodology Trial

Background: Previous studies indicated that affect fluctuations, the use of antidepressant medication (ADM), as well as depression during pregnancy might have adverse effects on offspring outcomes. The aim of the current proof-of-principle study is to explore the effect of tapering ADM while receiving online preventive cognitive therapy (PCT) on pregnant women and the offspring as compared to pregnant women continuing ADM. Objectives: We sought to compare positive and negative affect fluctuations in pregnant women receiving online PCT while tapering ADM vs. pregnant women continuing ADM, and to investigate if affect fluctuations in early pregnancy were related to offspring birth weight. Method: An experience sampling methodology (ESM)-trial ran alongside a Dutch randomized controlled trial (RCT) and prospective observational cohort of women using ADM at the start of pregnancy. In the ESM-trial fluctuations of positive and negative affect were assessed in the first 8 weeks after inclusion. Recurrences of depression were assessed up to 12 weeks post-partum, and birth records were used to assess offspring birth weight. The RCT has been registered at the Netherlands Trial Register (NTR4694, https://www.trialregister.nl/trial/4551). Results: In total, 19 pregnant women using ADM at start of their pregnancy participated in the ESM-trial. There were no significant differences in positive and negative affect fluctuations, nor recurrence rates between women receiving PCT while tapering ADM vs. women continuing ADM. We found no association between affect fluctuations, pre-natal depressive symptoms, and birth weight (all p > 0.05). Conclusion: This explorative study showed that tapering ADM while receiving online PCT may protect pregnant women against recurrences of depression and affect fluctuations, without affecting birth weight. There is a high need for more controlled studies focusing on tapering ADM with (online) psychological interventions during pregnancy.

Redefining Therapeutic Outcomes of Depression Treatment

Responses to evidence-based interventions for depression are divergent: Some patients benefit more than others during treatment and some do not benefit at all or even deteriorate. Tailoring interventions to the individual may improve outcomes. However, such personalization of evidence-based treatment in depression requires investigation of individual outcomes and the individual trajectories towards these outcomes. This theoretical paper provides a critical reflection on individual outcomes of depression treatment. First, it is argued that outcomes should be broadened, from a focus on mainly depressive symptomatology to recovery in different domains. It is acknowledged that recovery from depression reflects a personal journey that differs from person to person. Second, outcome measures should be lengthened beyond the acute treatment phase, taking a lifetime perspective on depression. The challenge then is to discover which trajectories of what measures during what interventions result in personalized sustainable recovery and for whom. Routine outcome monitoring systems may be used to inform this quest towards assessment of personalized sustainable therapeutic outcomes. Adaptations to broaden and lengthen measurements in routine outcome monitoring systems are proposed to identify predictors of personalized sustainable recovery. Routine outcome monitoring systems may eventually be used to implement personalized treatments for depression that result in personalized sustainable recovery.

Using the time-varying autoregressive model to study dynamic changes in situation perceptions and emotional reactions

OBJECTIVE

Assuming personality to be a system of intra-individual processes emerging over time in interaction with the environment, we propose an idiographic approach to investigate potential changes of intra-individual dynamics in the perception of situations and emotions of individuals varying in personality traits. We compared the semiparametric time-varying autoregressive model (TV-AR) that takes into account the non-stationarity of psychological processes at the individual level, with the standard AR model.

METHOD

We conducted analyses of individual time series to assess intra-individual changes in mean levels and inertia on data from two adolescents who completed measures of personality and indicated their situation perceptions and emotions five times a day for 19 days.

RESULTS

For the less honest, emotional, extraverted, and more agreeable adolescent, the TV-AR model detected reliable changes in the intra-individual dynamics of situation perceptions and emotions whereas, for the other individual, the standard AR model was more preferred, given the lack of changes in the intra-individual dynamics.

CONCLUSIONS

Psychological processes dynamics in situation perception and emotions may vary from person to person depending on their personality. This work constitutes a first step in demonstrating that an idiographic approach has advantages in identifying changes in individuals' perceptions and reactions to situations.

PTSD Symptom Clusters and Craving Differs by Primary Drug of Choice

Objective: Research has demonstrated a cyclical relationship between posttraumatic stress disorder (PTSD) and substance use disorder. Identifying factors that link PTSD symptom clusters and substance use disorder may illuminate mechanisms underlying the PTSD-substance use disorder relationship, better informing interventions that target this comorbidity. The current study of individuals enrolled in an outpatient aftercare chemical dependency program in King County, Washington, assessed whether overall PTSD symptoms and specific PTSD symptom clusters predicted craving depending on individuals identified primary drug of choice (DOC). Methods: Participants eligible for the parent study were at least 18 years of age, fluent in English, medically cleared from substance withdrawal, and able to participate in treatment sessions and agreed to random assignment. Random assignment to either a mindfulness-based relapse prevention group, a standard relapse prevention group, or a treatment as usual group was conducted on a computer randomization program. A secondary analysis of baseline data was employed in the current study to determine which of the PTSD symptom clusters (avoidance, hyperarousal, and intrusion) predicted substance craving. Results: Covarying for severity of dependence, results suggest that overall PTSD scores predicted craving in participants who identified alcohol, stimulants, and opiates as their primary DOC. Further, avoidance-related PTSD symptoms alone predicted a significant proportion of the variability in craving in stimulant users, and hyperarousal symptoms alone predicted a significant proportion of the variability in craving in alcohol users. No specific PTSD cluster significantly predicted a proportion of the variability in craving in marijuana or opiates users. Conclusions: Findings suggest that craving may play a role in maintaining the relationship between specific PTSD symptom clusters and substance use disorder, and the nature of this relationship may differ by primary DOC. The clinical trial on which this secondary analysis of data was conducted is registered as NCT01159535 at www.clinicaltrials.gov.The original trial from which data for this study was drawn was supported by the National Institutes of Health [NIH/NIDA 5 R01 DA025764-02].