Exploring relapse through a network analysis of residual depression and anxiety symptoms after cognitive behavioural therapy: A proof-of-concept study

Efficacy of an internet-delivered cognitive enhancement intervention for subjective residual cognitive deficits in remitted major depressive disorder: A randomized crossover trial

BACKGROUND

Cognitive deficits such as difficulties with attention, memory, and executive functions are frequently reported during remission from depression and relates to adverse functioning in daily life and risk of relapse. There is therefore a need for interventions targeting cognitive deficits after depression. However, few randomized controlled trials have investigated the efficacy of interventions targeting subjective residual cognitive deficits in adults remitted from depression.

METHODS

This randomized crossover trial aimed to investigate the efficacy of an internet-delivered cognitive enhancement intervention on subjective residual cognitive deficits. Forty-four formerly depressed adults (89 % female;mean age = 39 years) were included. Twenty-three participants received the intervention, and 21 participants were assigned to a waitlist control group. The waitlist control group received the intervention after seven weeks. Analyses of follow-up assessment after six months were conducted for the combined sample.

RESULTS

Significant differences were found between the intervention and waitlist control group in subjective cognitive functioning (d = 1.83) and rumination (d = 1.65). There was a difference in symptoms of depression between the groups (d = 1.22), whereas symptoms of depression increased in the waitlist control, but not in the intervention group. Fewer participants in the waitlist control group (43 %), compared to the intervention group (78 %) showed reliable improvement in self-reported cognitive deficits after receiving the intervention.

LIMITATIONS

Findings should be interpreted with caution due to the small sample, and lack of an active control group.

CONCLUSIONS

Internet-delivered cognitive enhancement interventions may improve subjective cognitive deficits. Waiting time to receive cognitive enhancement interventions may worsen symptoms and treatment response.

Comparing network structures of depressive and anxiety symptoms between demographically-matched heart disease and heart disease free samples using propensity score matching: Findings from the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BACKGROUND

Older adults with heart disease often experience higher rates of comorbid anxiety and depression. This study examined depression and anxiety network structures among older adults with heart disease and their heart disease free peers.

METHODS

Network analyses of secondary cross-sectional data from the 2017 to 2018 wave of CLHLS were used to construct groups of older adults with and without heart disease using propensity score matching. Depression and anxiety symptoms were assessed using Center for Epidemiological Studies Depression Scale and Generalized Anxiety Disorder Scale, respectively. Central symptoms and bridge symptoms were identified using expected influence.

RESULTS

1689 older adults with heart disease and matched control sample of 1689 older adults without heart disease were included. The prevalence and severity of depression and anxiety were significantly higher in older adults with heart disease compared to the control group. There was no significant difference in overall structures of depression and anxiety network models between two the groups. Key central symptoms and bridge symptoms within these groups were highly similar; GAD 2 "Uncontrollable worrying" and GAD 4 "Trouble relaxing" were identified as the most central symptoms, while GAD 1 "Nervousness" and CESD 1 "Feeling bothered" were identified as key bridge symptoms across both network models.

CONCLUSION

Depression and anxiety are more prevalent in older adults with heart disease than demographically-matched heart disease free controls. However, network structures of these symptoms do not differ between two groups. Accordingly, depression and anxiety psychosocial interventions developed for older adults without heart disease may also benefit older adults with heart disease.

Investigating change in network structure of eating disorder symptoms after delivery of a smartphone app-based intervention

BACKGROUND

Eating disorder (ED) research has embraced a network perspective of psychopathology, which proposes that psychiatric disorders can be conceptualized as a complex system of interacting symptoms. However, existing intervention studies using the network perspective have failed to find that symptom reductions coincide with reductions in strength of associations among these symptoms. We propose that this may reflect failure of alignment between network theory and study design and analysis. We offer hypotheses for specific symptom associations expected to be disrupted by an app-based intervention, and test sensitivity of a range of statistical metrics for identifying this intervention-induced disruption.

METHODS

Data were analyzed from individuals with recurrent binge eating who participated in a randomized controlled trial of a cognitive-behavioral smartphone application. Participants were categorized into one of three groups: waitlist (n = 155), intervention responder (n = 49), and intervention non-responder (n = 77). Several statistical tests (bivariate associations, network-derived strength statistics, network invariance tests) were compared in ability to identify change in network structure.

RESULTS

Hypothesized disruption to specific symptom associations was observed through change in bivariate correlations from baseline to post-intervention among the responder group but were not evident from symptom and whole-of-network based network analysis statistics. Effects were masked when the intervention group was assessed together, ignoring heterogeneity in treatment responsiveness.

CONCLUSION

Findings are consistent with our contention that study design and analytic approach influence the ability to test network theory predictions with fidelity. We conclude by offering key recommendations for future network theory-driven interventional studies.

Treatment Response Following Adaptive PASAT Training for Depression Vulnerability: a Systematic Review and Meta-Analysis

In recent years, cognitive control training (CCT) has gained momentum as an intervention to remediate cognitive impairments and decrease depressive symptoms. One promising operationalization to train cognitive control is the adaptive Paced Auditory Serial Addition Task (aPASAT). In this systematic review and meta-analysis of aPASAT training, the efficacy of the intervention and potential moderators were examined. The PsycINFO, MEDLINE, Embase, Web of Science and Cochrane Library electronic databases were searched for studies examining aPASAT training for depressive symptomatology or rumination. Nineteen studies (n = 1255) were included, comprising of depressed patients, remitted depressed patients, at-risk, and healthy participants. We found small significant effects directly after training for both depressive symptomatology and rumination, with similar effect sizes at follow-up. Subgroup analyses suggest a significantly higher mean effect of aPASAT training in non-healthy populations for rumination immediately following training, but not for depressive symptomatology. The amount of training sessions did not moderate effects of CCT. aPASAT has a small but significant effect on depressive symptoms, with direct effects immediately after training, as well as sustained long-term effects. It is currently unclear how many sessions are required for sustained effects due to heterogeneity in training dosage and absence of sufficient trials. Our results suggest that aPASAT training may be most effective for at-risk, remitted- and clinically depressed populations. The effect sizes resulting from this meta-analysis could be used to adequately power future research, which could investigate a dose-response relationship and examine potential treatment gains when combining CCT with other antidepressant interventions.

A network model of depressive and anxiety symptoms: a statistical evaluation

BACKGROUND

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

METHODS

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

RESULTS

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.

CONCLUSION

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

Development of decision rules for an adaptive aftercare intervention based on individual symptom courses for agoraphobia patients

As other mental illnesses, agoraphobia is associated with a significant risk for relapse after the end of treatment. Personalized and adaptive approaches appear promising to improve maintenance treatment and aftercare as they acknowledge patients' varying individual needs with respect to intensity of care over time. Currently, there is a deficit of knowledge about the detailed symptom course after discharge from acute treatment, which is a prerequisite for the empirical development of rules to decide if and when aftercare should be intensified. Therefore, this study aimed firstly at the investigation of the naturalistic symptom course of agoraphobia after discharge from initial treatment and secondly at the development and evaluation of a data-driven algorithm for a digital adaptive aftercare intervention. A total of 56 agoraphobia patients were recruited in 3 hospitals. Following discharge, participants completed a weekly online monitoring assessment for three months. While symptom severity remained stable at the group level, individual courses were highly heterogeneous. Approximately two-thirds of the patients (70%) reported considerable symptoms at some time, indicating a need for medium or high-intense therapeutic support. Simulating the application of the algorithm to the data set resulted in an early (86% before week six) and relatively even allocation of patients to three groups (need for no, medium, and high-intense support respectively). Overall, findings confirm the need for adaptive aftercare strategies in agoraphobia. Digital, adaptive approaches may provide immediate support to patients who experience symptom deterioration and thus promise to contribute to an optimized allocation of therapeutic resources and overall improvement of care.

Longitudinal interactions between residual symptoms and physiological stress in the remitted symptom network structure of depression

Residual symptoms and stress are amongst the most reliable predictors of relapse in remitted depression. Standard methodologies often preclude continuous stress sampling or the evaluation of complex symptom interactions. This limits knowledge acquisition relative to the day-to-day interactions between residual symptoms and stress. The study aims to explore the interactions between physiological stress and residual symptoms network structure in remitted depression. Twenty-two individuals remitted from depression completed baseline, daily diary (DD), and post-DD assessments. Self-reported stress and residual symptoms were measured at baseline and post-DD. Daily diaries required participants to use a wearable electrodermal activity (EDA) device during waking hours and complete residual symptom measures twice daily for 3-weeks. Two-step multilevel vector auto-regression models were used to estimate contemporaneous and dynamic networks. Depressed mood and concentration problems were central across networks. Skin conductance responses (SCRs), suicide, appetite, and sleep problems were central in the temporal and energy loss in the contemporaneous network. Increased SCRs predicted decreased energy loss. Residual symptoms and stress showed bi-directional interactions. Overall, depressed mood and concentration problems were consistently central, thus potentially important intervention targets. Non-obtrusive bio-signal measures should be used to provide the clinical evidence-base for modelling the interactions between depressive residual symptoms and stress. Practical implications are discussed throughout related to focusing on symptom-specific interactions in clinical practice, simultaneously reducing residual symptom and stress occurrences, EDA as pioneering signal for stress detection, and the central role of specific residual symptoms in remitted depression.

Health Economic Evaluation of Cognitive Control Training for Depression: Key Considerations

Depression is a serious and burdensome psychiatric illness that contributes heavily to health expenditures. These costs are partly related to the observation that depression is often not limited to a single episode but can recur or follow a chronic pathway. In terms of risk factors, it is acknowledged that cognitive impairments play a crucial role in vulnerability to depression. Within this context, cognitive control training (CCT) has shown its effectiveness in reducing the risk for recurrence of depression. CCT is low cost intensive and can be provided as a web-based intervention, which makes it easy to disseminate. Despite increasing interest in the field, studies examining the cost-effectiveness of CCT in the context of depression are largely missing. Health economic evaluation (HEE) allows to inform decision makers with evidence-based insights about how to spend limited available (financial) resources in the most efficient way. HEE studies constitute a crucial step in the implementation of a new intervention in clinical practice. Approaching preventive measures for depression such as CCT from an HEE perspective is informative to health policy, fostering optimal use of health expenditures. The aim of this paper was to inform and guide researchers during the phase of designing HEE studies in the context of CCT for depression. A clear view of CCT's cost-effectiveness is paramount for its clinical implementation.

Long-term outcomes of an internet-delivered cognitive enhancement intervention targeting residual cognitive deficits after major depressive disorder: a 2-year follow-up of an open trial

Background

Cognitive deficits are common and disabling residual symptoms following major depressive disorder (MDD) and are related to increased risk of relapse. Residual cognitive deficits should thus be considered an important target for treatment. However, few have reported long-term outcomes of interventions targeting residual cognitive deficits.

Objective

This study aimed to (1) investigate change between pre-treatment and 2-year follow-up assessments in cognitive deficits, rumination, and symptoms of MDD after an internet-delivered intervention targeting residual cognitive deficits; (2) to investigate stability in outcomes between 6-month and 2-year follow-up assessments; (3) to report the number of participants' experiencing a new episode of MDD in the follow-up period; and (4) to investigate differences in outcomes between those who experienced a new episode of MDD and those who did not.

Methods

A total of 43 partly remitted adults were included to test a guided internet-delivered intervention, which consisted of 10 modules involving psychoeducation, cognitive strategies, and attention training. Participants were assessed at pre-treatment, post-treatment, after 6-months, and after 2-years, with measures assessing self-reported residual cognitive deficits, rumination, symptoms of MDD and relapse. Overall, 32 participants completed the 2-year follow-up assessment.

Results

Between the pre-treatment and 2-year follow-up assessments, there was a reduction in cognitive deficits and rumination, while there was an increase in symptoms of MDD. Cognitive deficits were stable between the 6-month and the 2-year follow-up, while there was an increase in rumination and symptoms of MDD. Thirteen of 32 participants reported a new episode of MDD during the follow-up period. The relapse group reported longer duration of MDD at pre-treatment and showed a difference in all outcomes after 2 years compared to the no-relapse group. The no-relapse group showed improvement in MDD symptoms at post-treatment, while the relapse group did not.

Conclusion

Delivering cognitive enhancement interventions over the internet is potentially related to stable improvements in residual cognitive deficits. The effects on rumination and symptoms of MDD are less certain. Lack of improvement in MDD symptoms after the intervention period should be investigated as an indicator of relapse. Results should be interpreted with caution due to the lack of control group and sample size.

Symptom-specific effects of counselling for depression compared to cognitive-behavioural therapy

BACKGROUND

Cognitive-behavioural therapy (CBT) and counselling for depression (CfD) are recommended first-line treatments for depression. While they approach change differently, there is little understanding of the impact those approaches have on change during treatment.

OBJECTIVES

This study aimed to identify whether CBT and CfD target different symptoms and explore the implications of modelling choices when quantifying change during treatment.

METHODS

Symptom-specific effects of treatment were identified using moderated network modelling. This was a retrospective cohort study of 12 756 individuals who received CBT or CfD for depression in primary/community care psychological therapy services in England. Change was modelled several ways within the whole sample and a propensity score matched sample (n=3446).

FINDINGS

CBT for depression directly affected excessive worry, trouble relaxing and apprehensive expectation and had a stronger influence on changes between suicidal ideation and concentration. CfD had a stronger direct influence on thoughts of being a failure and on the associated change between being an easily annoyed and apprehensive of expectation. There were inconsistencies when modelling change using the first and second appointments as the baseline. Residual score models produced more conservative findings than models using difference scores.

CONCLUSIONS

CfD and CBT for depression have differential effects on symptoms demonstrating specific mechanisms of change.

CLINICAL IMPLICATIONS

CBT was uniquely associated with changes in symptoms associated with anxiety and may be better suited to those with anxiety symptoms comorbid to their depression. When assessing change, the baseline should be the first therapy session, not the pretreatment assessment. Residual change scores should be preferred over difference score methods.

Does Symptom Linkage Density Predict Outcomes in Cognitive Therapy for Recurrent Depression?

Background

Acute-phase cognitive therapy (CT) is an efficacious treatment for major depressive disorder (MDD), but how CT helps patients is incompletely understood. As a potential means to clarify CT mechanisms, we defined "symptom linkage density" (SLD) as a patient's mean time-lagged correlation among nine depressive symptoms across 13 weekly assessments. We hypothesized that patients with higher SLD during CT have better outcomes (treatment response, and fewer symptoms after response), and we explored whether SLD correlated with other possible CT processes (growth in social adjustment and CT skills).

Method

Data were drawn from two clinical trials of CT for adult outpatients with recurrent MDD (primary sample n = 475, replication sample n = 146). In both samples, patients and clinicians completed measures of depressive symptoms and social adjustment repeatedly during CT. In the primary sample, patients and cognitive therapists rated patients' CT skills. After CT, responders were assessed for 32 (primary sample) or 24 (replication sample) additional months to measure long-term depression outcomes.

Results

Higher SLD predicted increases in social adjustment (both samples) and CT skills (primary sample) during CT, CT response (both samples), and lower MDD severity for at least 2 years after CT response (both samples). Analyses controlled patient-level symptom means and variability to estimate SLD's incremental predictive validity.

Conclusions

These novel findings from two independent samples with longitudinal follow-up require further replication and extension. SLD may reflect or facilitate generalization of CT skills, improvement in social functioning, or other processes responsible for CT's shorter and longer term benefits.

A Systematic Review and Individual Patient Data Network Analysis of the Residual Symptom Structure Following Cognitive-Behavioral Therapy and Escitalopram, Mirtazapine and Venlafaxine for Depression

OBJECTIVE

Consistent evidence suggests residual depressive symptomology are the strongest predictors of depression relapse following cognitive-behavioral therapy (CBT) and antidepressant medications (ADM's). Psychometric network models help detecting and understanding central symptoms that remain post-treatment, along with their complex co-occurrences. However, individual psychometric network studies show inconsistent findings. This systematic review and IPD network analysis aimed to estimate and compare the symptom network structures of residual depressive symptoms following CBT, ADM's, and their combination.

METHODS

PsycINFO, PsycArticles, and PubMed were systematically searched through October 2020 for studies that have assessed individuals with major depression at post-treatment receiving either CBT and/or ADM's (venlafaxine, escitalopram, mirtazapine). IPD was requested from eligible samples to estimate and compare residual symptom psychometric network models post-CBT and post-ADM's.

RESULTS

In total, 25 from 663 eligible samples, including 1,389 patients qualified for the IPD. Depressed mood and anhedonia were consistently central residual symptoms post-CBT and post-ADM's. For CBT, fatigue-related and anxiety symptoms were also central post-treatment. A significant difference in network structure across treatments (CBT vs. ADM) was observed for samples measuring depression severity using the MADRS. Specifically, stronger symptom occurrences were present amongst lassitude-suicide post-CBT (vs. ADM's) and amongst lassitude-inability to feel post-ADM's (vs. CBT). No significant difference in global strength was observed across treatments.

CONCLUSIONS

Core major depression symptoms remain central across treatments, strategies to target these symptoms should be considered. Anxiety and fatigue related complaints also remain central post-CBT. Efforts must be made amongst researchers, institutions, and journals to permit sharing of IPD.Systematic Review Registration: A protocol was prospectively registered on PROSPERO (CRD42020141663; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=141663).

Characteristics associated with the discrepancy between subjective and objective executive functioning in depression

Major depressive disorder (MDD) is associated with both self-reported (subjective) cognitive complaints and deficits in neurocognitive (objective) measures, but the correspondence between subjective and objective measures of cognition is low. This cross-sectional study aimed to (1) assess the association between subjective and objective measures of executive functions (EFs), and (2) explore factors associated with the discrepancy between subjective and objective EFs in MDD. Sixty-two participants with current or previous mild to moderate MDD and subjective EF complaints completed a clinical trial baseline assessment. An objective EF composite score was drawn from six neurocognitive measures, while the Behavior Rating Inventory of Executive Function-Adult version was applied as a measure of subjective EF. The association between the subjective and objective composites was evaluated using Spearman's rank order correlation. A discrepancy score was calculated to quantify the difference between subjective and objective EF. Factors associated with the discrepancy score were analyzed using regression analysis (p < .05). Participants reported extensive EF difficulties, but most performed in the normal range on objective EF measures. A weak correlation was detected between the subjective and objective measures (r_s = .015). More rumination (β = -.364) and higher IQ (β = -.420) were associated with reporting more subjective complaints than was evident from objective measures of EF (i.e., underestimation). Subjective and objective EF measures are weakly overlapping in MDD. Findings underscore recommendations to include both subjective and objective measures when assessing EFs in depression. In addition, findings suggest that targeting ruminative processes could help correct underestimation.

How do you feel? Using natural language processing to automatically rate emotion in psychotherapy

Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account. However, recent advances in technology in the area of machine learning algorithms, in particular natural language processing, have made it possible for mental health researchers to identify sentiment, or emotion, in therapist-client interactions on a large scale that would be unattainable with more traditional methods. As an attempt to extend prior findings from Tanana et al. (2016), we compared their previous sentiment model with a common dictionary-based psychotherapy model, LIWC, and a new NLP model, BERT. We used the human ratings from a database of 97,497 utterances from psychotherapy to train the BERT model. Our findings revealed that the unigram sentiment model (kappa = 0.31) outperformed LIWC (kappa = 0.25), and ultimately BERT outperformed both models (kappa = 0.48).

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.