Understanding heterogeneity, comorbidity, and variability in depression: Idiographic models and depression outcomes

This study uses time-intensive, item-level assessment to examine individual depressive and co-occurring symptom dynamics. Participants experiencing moderate-severe depression (N = 31) completed ecological momentary assessment (EMA) four times per day for 20 days (total observations = 2480). We estimated idiographic networks using MDD, anxiety, and ED items. ED items were most frequently included in individual networks relative to depression and anxiety items. We built ridge and logistic regression ensembles to explore how idiographic network centrality metrics performed at predicting between-subject depression outcomes (PHQ-9 change score and clinical deterioration, respectively) at 6-months follow-up. For predicting PHQ-9 change score, R2 ranged between 0.13 and 0.28. Models predicting clinical deterioration ranged from no better than chance to 80 % accuracy. This pilot study shows how co-occurring anxiety and ED symptoms may contribute to the maintenance of depressive symptoms. Future work should assess the predictive utility of psychological networks to develop understanding of how idiographic models may inform clinical decisions.

Bedtime regularity predicts positive affect among veterans with posttraumatic stress disorder: an ecological momentary assessment study

BACKGROUND

Regularizing bedtime and out-of-bed times is a core component of behavioral treatments for sleep disturbances common among patients with posttraumatic stress disorder (PTSD). Although improvements in subjective sleep complaints often accompany improvements in PTSD symptoms, the underlying mechanism for this relationship remains unclear. Given that night-to-night sleep variability is a predictor of physical and mental well-being, the present study sought to evaluate the effects of bedtime and out-of-bed time variability on daytime affect and explore the optimal window lengths of over which variability is calculated.

METHODS

For about 30 days, male U.S. military veterans with PTSD (N = 64) in a residential treatment program provided ecological momentary assessment data on their affect and slept on beds equipped with mattress actigraphy. We computed bedtime and out-of-bed time variability indices with varying windows of days. We then constructed multilevel models to account for the nested structure of our data and evaluate the impact of bedtime and out-of-bed time variability on daytime affect.

RESULTS

More regular bedtime across 6-9 days was associated with greater subsequent positive affect. No similar effects were observed between out-of-bed time variability and affect.

CONCLUSIONS

Multiple facets of sleep have been shown to differently predict daily affect, and bedtime regularity might represent one of such indices associated with positive, but not negative, affect. A better understanding of such differential effects of facets of sleep on affect will help further elucidate the complex and intertwined relationship between sleep and psychopathology.

TRIAL REGISTRATION

The trial retrospectively was registered on the Defense Technical Information Center website: Award # W81XWH-15-2-0005.

Elevated emotion network connectivity is associated with fluctuations in depression

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

Maximizing organs for donation: the potential for ex situ normothermic machine perfusion

Currently, there is a shortfall in the number of suitable organs available for transplant resulting in a high number of patients on the active transplant waiting lists worldwide. To address this shortfall and increase the utilization of donor organs, the acceptance criteria for donor organs is gradually expanding including increased use of organs from donation after circulatory death. Use of such extended criteria donors and exposure of organs to more prolonged periods of warm or cold ischaemia also increases the risk of primary graft dysfunction occurring. Normothermic machine perfusion (NMP) offers a unique opportunity to objectively assess donor organ function outside the donor body and potentially recondition those deemed unsuitable on initial evaluation prior to implantation in the recipient. Furthermore, NMP provides a platform to support the use of established and novel therapeutics delivered directly to the organ, without the need to worry about potential deleterious 'off-target' side effects typically considered when treating the whole patient. This review will explore some of the novel therapeutics currently being added to perfusion platforms during NMP experimentally in an attempt to improve organ function and post-transplant outcomes.

Sleep Quality Moderates the Relationship Between Daily Mean Levels and Variability of Positive Affect

Despite the well-established bidirectional association between sleep and daytime affect, most studies examining this relationship have focused on mean levels of affect. However, research solely focusing on mean levels of affect inherently neglects variability in affect, which has been shown to predict both psychological and physical well-being beyond mean levels. The present study assessed sleep quality and daytime affect using ecological momentary assessment in a combined sample of individuals (N = 80; 8,881 observations) with and without anxiety and mood disorders. Results from the present study partially replicated extant work on the negative association between negative affect (NA) variability and subsequent sleep quality. Furthermore, less satisfying sleep amplified the positive relationship between daily mean levels and variability of positive affect (PA). The results did not differ by clinical status. The present study offers novel evidence suggesting that previous night's sleep quality influences the stability of varying daily levels of PA. Uncovering the dynamics of sleep and affect beyond mean levels will help further elucidate mechanisms linking sleep and subsequent affective experiences.

Clinician Opinions: Perceived Utility of and Barriers to Incorporating Pretreatment Ecological Momentary Assessment Into Clinical Care

Increasingly, clinicians have the option of including technological components into clinical care. However, little research has assessed clinicians' interest in utilizing technology in their clinical work. Here, clinicians reported their opinions related to using a mobile assessment platform (MAP) to collect ecological data from clients before providing clinical care. Practicing and training mental health clinicians (N = 221) reported demographics, characteristics of their clinical work, and confidence in their clinical skill. Participants then read a description of MAP and responded to questions about their perceived benefits of and barriers to its use. Last, participants rated their interest in using MAP in their clinical work. These perceptions were then factor-analyzed and the resulting factor scores were regressed onto clinician characteristics. Interest in using MAP was significantly lower for the group that endorsed a psychodynamic/psychoanalytic orientation and those with greater confidence in their clinical skills. Across scales, we found a pattern that participants who did not identify as male, those with a psychodynamic/psychoanalytic orientation, and those with greater confidence in their clinical skills tended to have lower ratings of the benefits of and higher ratings for the barriers to using MAP. Results revealed that significant differences in opinions about incorporating technology into clinical work exist between different groups of clinicians. This information may be useful in future work that attempts to implement technological tools into clinical settings.

Identifying and predicting posttraumatic stress symptom states in adults with posttraumatic stress disorder

Between-person heterogeneity of posttraumatic stress disorder (PTSD) is well established. Within-person analyses and the DSM-5 suggest that heterogeneity may also be evident within individuals across time as they move through social contexts and biological cycles. Modeling within-person symptom-level fluctuations may confirm such heterogeneity, elucidate mechanisms of disorder maintenance, and inform time- and person-specific interventions. The present study aimed to identify and predict discrete within-person disorder presentations, or symptom states, and explore group-level patterns of these states. Adults (N = 20, 60.0% male, M age = 38.25 years) with PTSD responded to symptom surveys four times per day for 30 days. We subjected each individual's dataset to Gaussian finite mixture modeling (GFMM) to uncover latent, within-person classes of symptom levels (i.e., states) and predicted those states with idiographic elastic net regularized regression using a set of time-based and behavioral predictors. Next, we conducted a GFMM of the within-person GFMM outputs and tested idiographic prediction models of these states. Multiple within-person states were revealed for 19 of 20 participants (Mdn = 4; 66 for the full sample). Prediction models were moderately successful, M AUC = .66 (d = 0.58), range: .50-1.00. The GFMM of the within-person model outputs revealed two states: one with above-average and one with below-average symptom levels. Prediction models were, again, moderately successful, M AUC = .66; range: .50-.89. The findings provide evidence for within-person heterogeneity of PTSD as well as between-person similarities and suggest that future work should incorporate additional contextual variables as symptom state predictors.

Examining the Concurrent and Predictive Validity of Single Items in Ecological Momentary Assessments

Although single items can save time and burden in psychology research, concerns about their reliability have made the use of multiple-item measures the default standard practice. Although single items cannot demonstrate internal reliability, their criterion validity can be compared with multiple-item measures. Using ecological momentary assessment data, we evaluated repeated measures correlations and constructed multilevel cross-lagged models to assess concurrent and predictive validity of single- and multiple-item measures. Correlations between the single- and multiple-item measures ranged from .24 to .61. In 27 of 29 unique single-item predictor models, single items demonstrated significant predictive validity, and in one of eight sets of comparisons, a single-item predictor exhibited a larger effect size than its multiple-item counterpart. Although multiple-item measures generally performed better than single items, the added benefit of multiple items was modest in most cases. The present data provide support for the use of single-item measures in intensive longitudinal designs.

Examining the Concurrent and Predictive Validity of Single Items in Ecological Momentary Assessments

Although single items can save time and burden in psychology research, concerns about their reliability have made the use of multiple-item measures the default standard practice. Although single items cannot demonstrate internal reliability, their criterion validity can be compared with multiple-item measures. Using ecological momentary assessment data, we evaluated repeated measures correlations and constructed multilevel cross-lagged models to assess concurrent and predictive validity of single- and multiple-item measures. Correlations between the single- and multiple-item measures ranged from .24 to .61. In 27 of 29 unique single-item predictor models, single items demonstrated significant predictive validity, and in one of eight sets of comparisons, a single-item predictor exhibited a larger effect size than its multiple-item counterpart. Although multiple-item measures generally performed better than single items, the added benefit of multiple items was modest in most cases. The present data provide support for the use of single-item measures in intensive longitudinal designs.

The Hierarchical Taxonomy of Psychopathology (HiTOP) Is Not an Improvement Over the DSM

In their response to our article (both in this issue), DeYoung and colleagues did not sufficiently address three fundamental flaws with the Hierarchical Taxonomy of Psychopathology (HiTOP). First, HiTOP was created using a simple-structure factor-analytic approach, which does not adequately represent the dimensional space of the symptoms of psychopathology. Consequently, HiTOP is not the empirical structure of psychopathology. Second, factor analysis and dimensional ratings do not fix the problems inherent to descriptive (folk) classification; self-reported symptoms are still the basis on which clinical judgments about people are made. Finally, HiTOP is not ready to use in real-world clinical settings. There is currently no empirical evidence demonstrating that clinicians who use HiTOP have better clinical outcomes than those who use the Diagnostic and Statistical Manual of Mental Disorders (DSM). In sum, HiTOP is a factor-analytic variation of the DSM that does not get the field closer to a more valid and useful taxonomy.

Folk Classification and Factor Rotations: Whales, Sharks, and the Problems With the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group people with similar self-reported symptoms (i.e., like-goes-with-like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory-building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the DSM is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system.

Negative Emotion Differentiation Predicts Psychotherapy Outcome: Preliminary Findings

Emotion differentiation (ED), the extent to which same-valenced emotions are experienced as distinct, is considered a valuable ability in various contexts owing to the essential affect-related information it provides. This information can help individuals understand and regulate their emotional and motivational states. In this study, we sought to examine the extent to which ED can be beneficial in psychotherapy context and specifically for predicting treatment response. Thirty-two prospective patients with mood and anxiety disorders completed four daily assessments of negative and positive emotions for 30 days before receiving cognitive-behavioral treatment. Depression, stress, and anxiety symptoms severity were assessed pre- and post-treatment using self-reports and clinical interviews. We conducted a series of hierarchical regression models in which symptoms change scores were predicted by ED while adjusting for the mean and variability. We found that negative ED was associated with greater self-reported treatment response (except for anxiety) when negative emotional variability (EV) was included in the models. Probing negative ED and EV's interactive effects suggested that negative ED was associated with greater treatment response (except for anxiety) for individuals with lower EV levels. Results were obtained while controlling for mean negative affect. Our findings suggest that negative ED can benefit psychotherapy patients whose negative emotions are relatively less variable. We discuss the meaning of suppression and interactive effects between affect dynamics and consider possible clinical implications.

Pooled and person-specific machine learning models for predicting future alcohol consumption, craving, and wanting to drink: A demonstration of parallel utility

BACKGROUND AND AIMS

The specific factors driving alcohol consumption, craving, and wanting to drink, are likely different for different people. The present study sought to apply statistical classification methods to idiographic time series data in order to identify person-specific predictors of future drinking-relevant behavior, affect, and cognitions in a college student sample.

DESIGN

Participants were sent 8 mobile phone surveys per day for 15 days. Each survey assessed the number of drinks consumed since the previous survey, as well as positive affect, negative affect, alcohol craving, drinking expectancies, perceived alcohol consumption norms, impulsivity, and social and situational context. Each individual's data were split into training and testing sets, so that trained models could be validated using person-specific out-of-sample data. Elastic net regularization was used to select a subset of a set of 40 variables to be used to predict either alcohol consumption, craving, or wanting to drink, forward in time.

SETTING

A west-coast university.

PARTICIPANTS

Thirty-three university students who had consumed alcohol in their lifetime.

MEASUREMENTS

Mobile phone surveys.

FINDINGS

Averaging across participants, accurate out-of-sample predictions of future drinking were made 76% of the time. For craving, the mean out-of-sample R² value was .27. For wanting to drink, the mean out-of-sample R² value was .27.

CONCLUSION

Using a person-specific constellation of psychosocial and temporal variables, it may be possible to accurately predict drinking behavior, affect, and cognitions before they occur. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Microarray analysis of primary epithelial and fibroblast cells in chronic rhinosinusitis without nasal polyps

BACKGROUND

Chronic rhinosinusitis is an inflammatory condition with an as yet unknown pathophysiology. We aimed to detect clusters of differentially regulated genes in the epithelial and fibroblast cells of patients with Chronic Rhinosinusitis without nasal polyposis (CRSsNP) and healthy controls.

METHODOLOGY

Carefully phenotyped CRSsNP and healthy control participants were recruited. Primary cultures of isolated epithelial and fibroblast cells were established. Whole transcriptome analysis of the cells was performed using microarrays and replicated with quantitative RT-PCR and immunohistochemistry.

RESULTS

Fibroblast cells from CRSsNP patients showed a significant upregulation (more than 2x) of the transcription factor NFE2L3 when compared to healthy controls by microarray with multiple hypothesis testing correction, qRT-PCR and immunohistochemistry.

CONCLUSIONS

Here we have utilized microarray analysis to search for differentially expressed genes in isolated patient derived epithelial and fibroblast cells. The transcription factor NFE2L3 has been shown to be upregulated in fibroblast cells consistent with increasing evidence that fibroblasts play a key role in tissue specific inflammation within the paranasal sinuses.

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE

One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them.

METHODS

To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment.

RESULTS

Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely.

CONCLUSION

This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.

Idiographic Dynamics of Positive Affect in GAD

Abstract. Generalized anxiety disorder (GAD) is associated with worry and emotion regulation difficulties. The contrast-avoidance model suggests that individuals with GAD use worry to regulate emotion: by worrying, they maintain a constant state of negative affect (NA), avoiding a feared sudden shift into NA. We tested an extension of this model to positive affect (PA). During a week-long ecological momentary assessment (EMA) period, 96 undergraduates with a GAD analog provided four daily measurements of worry, dampening (i.e., PA suppression), and PA. We hypothesized a time-lagged mediation relationship in which higher worry predicts later dampening, and dampening predicts subsequently lower PA. A lag-2 structural equation model was fit to the group-aggregated data and to each individual time-series to test this hypothesis. Although worry and PA were negatively correlated in 87 participants, our model was not supported at the nomothetic level. However, idiographically, our model was well-fit for about a third (38.5%) of participants. We then used automatic search as an idiographic exploratory procedure to detect other time-lagged relationships between these constructs. While 46 individuals exhibited some cross-lagged relationships, no clear pattern emerged across participants. An alternative hypothesis about the speed of the relationship between variables is discussed using contemporaneous correlations of worry, dampening, and PA. Findings suggest heterogeneity in the function of worry as a regulatory strategy, and the importance of temporal scale for detection of time-lagged effects.

Why Does Therapy Work? An Idiographic Approach to Explore Mechanisms of Change Over the Course of Psychotherapy Using Digital Assessments

Background and Objective(s)

While psychotherapy treatments are largely effective, the processes and mechanisms underlying such positive changes remain somewhat unknown. Focusing on a single participant from a treatment outcome study that used a modular-based cognitive behavior therapy protocol, this article aims to answer this question by identifying changes in specific symptomatology over the course of the treatment. Using quantitative data derived from digital health methodology, we analyzed whether a given therapeutic intervention was related to downstream effects in predicted symptom domains, to assess the accuracy of our interventions.

Methods

This case study employed an observational N-of-1 study design. The participant (n = 1) was a female in the age range of 25–35 years. Using digital health data from ambulatory assessment surveys completed prior to and during therapy, separate linear regression analyses were conducted to assess if hypothesized treatment targets reduced after a given module, or intervention.

Results

Support was found for some of the hypothesized quantitative changes (e.g., decreases in avoidance after exposures module), yet not for others (e.g., decreases in rumination following the mindfulness module).

Conclusion

We present data and results from our analyses to offer an example of a novel design that may allow for a greater understanding of the nature of symptom changes with increased granularity throughout the course of a psychological treatment from the use of digital health tools.

An Examination of Idiographic Networks of Posttraumatic Stress Disorder Symptoms

Although the application of network theory to posttraumatic stress disorder (PTSD) has yielded promising insights, the lack of equivalence between inter- and intraindividual variation limits the generalizability of these findings to any one individual with PTSD. Instead, a better understanding of how PTSD symptoms occur and vary over time within an individual requires exploring the idiographic network structure of PTSD. To do so, the present study used an intensive repeated-measures design to estimate intraindividual networks of PTSD symptoms on a person-by-person basis. Participants were 20 individuals who met criteria for PTSD and completed daily surveys assessing PTSD symptoms; surveys were completed four times per day for approximately 30 days. Employing a recently validated method provided by Fisher, Reeves, Lawyer, Medaglia, and Rubel (2017), we used these data to estimate a contemporaneous and temporal network of PTSD symptoms for individuals on a person-by-person basis. We then calculated centrality metrics to determine the relative importance of each symptom in each idiographic network. Across all contemporaneous networks, negative trauma-related cognitions and emotions were most commonly the most central symptoms. Further, across all temporal networks, (a) negative trauma-related emotions were the most common driver of variation in other symptoms over time and (b) distressing trauma-related dreams and sleep disturbance were the most common downstream consequences of variation in other PTSD symptoms over time. We also reviewed data from two randomly selected participants to illustrate how this approach could be used to identify maintenance factors of PTSD for each individual and guide individual treatment planning.

Person-Specific Methods for Characterizing the Course and Temporal Dynamics of Concussion Symptomatology: A Pilot Study

Better characterization of acute concussion symptomatology is needed in order to advance clinical and scientific understanding of persistent concussion symptoms. This paper aims to illustrate a novel framework for conceptualizing, collecting, and analyzing concussion symptom data. To that end, we describe the temporal and structural dynamics of acute concussion symptoms at the individual-patient level. Ten recently concussion adolescents and young adults completed 20 days of ecological momentary assessment (EMA) of post-concussion symptoms. Follow-up assessments were completed at 3 months post-injury. Network modeling revealed marked heterogeneity across participants. In the overall sample, temporal patterns explained the most variance in light sensitivity (48%) and the least variance in vomiting (5%). About half of the participants had symptom networks that were sparse after controlling for temporal variation. The other individualized symptom networks were densely interconnected clusters of symptoms. Networks were highly idiosyncratic in nature, yet emotional symptoms (nervousness, emotional, sadness), cognitive symptoms (mental fogginess, slowness), and symptoms of hyperacusis (sensitivity to light, sensitivity to noise) tended to cluster together across participants. Person-specific analytic techniques revealed a number of idiosyncratic features of post-concussion symptomatology. We propose applying this framework to future research to better understand individual differences in concussion recovery.

Evidence for the Feasibility of Person-Specific Ecological Momentary Assessment Across Diverse Populations and Study Designs

Clinical psychological science has seen an exciting shift toward the use of person-specific (idiographic) approaches to studying psychopathology and change in treatment at the level of the individual. One commonly used method in idiographic research is ecological momentary assessment (EMA). EMA offers a way to sample individuals intensively – often multiple times per day – as they go about their lives. While these methods offer benefits such as greater ecological validity and streamlined data collection, many share concerns about their feasibility across diverse clinical populations. To investigate the feasibility of using EMA to study psychological processes idiographically both in- and out of the context of therapy, the present study aggregated participants across seven studies spanning diverse clinical and community populations (N = 496), all of which utilized an idiographic EMA approach to study symptoms of psychopathology (e.g., PTSD, mood and anxiety, substance abuse). In a series of linear regression models, participant and study design characteristics were used to predict compliance with EMA surveys. Across study designs, we found that (1) participants were willing to report on symptoms and mechanisms relating to a wide range of psychopathological domains; (2) on average, participants completed 82.21% (SD = 16.34%) of all EMA surveys; and (3) compliance with EMA surveys was not significantly related to participant demographics, psychological diagnosis, personality characteristics, or most study characteristics (e.g., number of surveys per day). These findings suggest feasibility of idiographic EMA for collecting the data needed to understand psychopathology and change in treatment at the level of the individual.

Affect Dynamics as Predictors of Symptom Severity and Treatment Response in Mood and Anxiety Disorders: Evidence for Specificity

Studies of affect dynamics in psychopathology often focus on the prediction of broad constructs like subjective well-being and psychological health. Less is known about how fluctuation in affect over time relates to specific symptom measures (e.g., anxiety or depression), or how these domains change in treatment. A clinical sample of 32 adults with mood and anxiety disorders (13 generalized anxiety, 5 major depression, 14 comorbid) completed four daily assessments of positive (PA) and negative affect (NA) for 30 days prior to receiving cognitive behavioral treatment. Anxiety and depression symptom severity were assessed pre- and post-treatment. We calculated three metrics of affect dynamics for each person’s PA and NA time series: (1) variability (experiencing emotional extremes, the standard deviation of a person’s PA or NA vector); (2) instability (magnitude of point-to-point change in emotion, the vector’s mean squared successive difference); and (3) inertia (the extent to which emotions self-perpetuate over time, the lag-1 autocorrelation of the vector). Multiple regression models were run to test dynamics of positive and negative affect as between-subjects predictors of symptom severity and pre-to-posttreatment change in symptoms. Findings suggest NA dynamics are unrelated to depression symptom severity or treatment response, but we observed a specific effect of NA instability (MSSD) on both severity and response of anxiety symptoms. All PA dynamics were unrelated to anxiety or depression symptom severity. However, variability, instability, and inertia of PA were all found to relate to treatment response for both anxiety and depression symptoms. Taken together, our results suggest that affect dynamics have some specificity in their relationship to clinically relevant phenomena such as symptom severity and treatment outcomes at the between-subjects level of analysis.

Exploring the idiographic dynamics of mood and anxiety via network analysis

Individual variation is increasingly recognized as important to psychopathology research. Concurrently, new methods of analysis based on network models are bringing new perspectives on mental (dys)function. This current work analyzed idiographic multivariate time series data using a novel network methodology that incorporates contemporaneous and lagged associations in mood and anxiety symptomatology. Data were taken from 40 individuals with generalized anxiety disorder (GAD), major depressive disorder (MDD), or comorbid GAD and MDD, who answered questions about 21 descriptors of mood and anxiety symptomatology 4 times a day over a period of approximately 30 days. The model provided an excellent fit to the intraindividual symptom dynamics of all 40 individuals. The most central symptoms in contemporaneous systems were those related to positive and negative mood. The temporal networks highlighted the importance of anger to symptomatology, while also finding that depressed mood and worry-the principal diagnostic criteria for GAD and MDD-were the least influential nodes across the sample. The method's potential for analysis of individual symptom patterns is demonstrated by 3 exemplar participants. Idiographic network-based analysis may fundamentally alter the way psychopathology is assessed, classified, and treated, allowing researchers and clinicians to better understand individual symptom dynamics. (PsycINFO Database Record

Incorporating ex-vivo lung perfusion into the UK adult lung transplant service: an economic evaluation and decision analytic model

Background

An estimated 20–30% of end-stage lung disease patients awaiting lung transplant die whilst on the waiting list due to a shortage of suitable donor lungs. Ex-Vivo Lung Perfusion is a technique that reconditions donor lungs initially not deemed usable in order to make them suitable for transplantation, thereby increasing the donor pool. In this study, an economic evaluation was conducted as part of DEVELOP-UK, a multi-centre study assessing the clinical and cost-effectiveness of the Ex-Vivo Lung Perfusion technique in the United Kingdom.

Methods

We estimated the cost-effectiveness of a UK adult lung transplant service combining both standard and Ex-Vivo Lung Perfusion transplants compared to a service including only standard lung transplants. A Markov model was developed and populated with a combination of DEVELOP-UK, published and clinical routine data, and extrapolated to a lifetime horizon. Probabilistic sensitivity and scenario analyses were used to explore uncertainty in the final outcomes.

Results

Base-case model results estimated life years gained of 0.040, quality-adjusted life-years (QALYs) gained of 0.045 and an incremental cost per QALY of £90,000 for Ex-Vivo Lung Perfusion. Scenario analyses carried out suggest that an improved rate of converting unusable donor lungs using Ex-Vivo Lung Perfusion, similar resource use post-transplant for both standard and EVLP lung transplant and applying increased waiting list costs would reduce ICERs to approximately £30,000 or below.

Conclusion

DEVELOP-UK base-case results suggest that incorporating Ex-Vivo Lung Perfusion into the UK adult lung transplant service is more effective, increasing the number of donor lungs available for transplant, but would not currently be considered cost-effective in the UK using the present NICE threshold. However, results were sensitive to change in some model parameters and in several plausible scenario analyses results indicate that a service incorporating Ex-vivo lung perfusion would be considered cost-effective .

Trial registration

ISRCTN registry number: ISRCTN44922411.

Date of registration: 06/02/2012.

Retrospectively registered.

Involving stakeholders in the design of ecological momentary assessment research: An example from smoking cessation

Ecological momentary assessment (EMA) is a data collection method that involves repeated sampling of participants’ real-time experience and behavior as they unfold in context. A primary challenge in EMA research is to design surveys that adequately assess constructs of interest while minimizing participant burden. To achieve this balance, researchers must make decisions regarding which constructs should be included and how those constructs should be assessed. To date, a dearth of direction exists for how to best design and carry out EMA studies. The lack of guidelines renders it difficult to systematically compare findings across EMA studies. Study design decisions may be improved by including input from potential research participants (stakeholders). The goal of the present paper is to introduce a general approach for including stakeholders in the development of EMA research design. Rather than suggesting rigid prescriptive guidelines (e.g., the correct number of survey items), we present a systematic and reproducible process through which extant research and stakeholder experience can be leveraged to make design decisions. To that end, we report methods and results for a series of focus group discussions with current tobacco users that were conducted to inform the design of an EMA study aimed at identifying person-specific mechanisms driving tobacco use. We conclude by providing recommendations for item-selection procedures in EMA studies.

Lack of group-to-individual generalizability is a threat to human subjects research

Only for ergodic processes will inferences based on group-level data generalize to individual experience or behavior. Because human social and psychological processes typically have an individually variable and time-varying nature, they are unlikely to be ergodic. In this paper, six studies with a repeated-measure design were used for symmetric comparisons of interindividual and intraindividual variation. Our results delineate the potential scope and impact of nonergodic data in human subjects research. Analyses across six samples (with 87-94 participants and an equal number of assessments per participant) showed some degree of agreement in central tendency estimates (mean) between groups and individuals across constructs and data collection paradigms. However, the variance around the expected value was two to four times larger within individuals than within groups. This suggests that literatures in social and medical sciences may overestimate the accuracy of aggregated statistical estimates. This observation could have serious consequences for how we understand the consistency between group and individual correlations, and the generalizability of conclusions between domains. Researchers should explicitly test for equivalence of processes at the individual and group level across the social and medical sciences.

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: A methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.

Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA)

Given the recent increase in transdiagnostic research, it is important to discern how dimensional models of psychopathology could be used to guide personalized, dynamic assessment and treatment of symptoms. Using the person-specific approach described by Fisher (2015), we devised an initial 4-step algorithm for devising a treatment plan based on modular cognitive behavioral therapy using results obtained from within-person factor analyses and dynamic factor models. Then, we describe the improvement and digitization of the algorithm, termed Dynamic Assessment Treatment Algorithm (DATA). The development, structure, and clinical implications of DATA are discussed.

Using social outcomes to inform decision-making in schizophrenia: Relationships with symptoms and functioning

The outcomes of the decisions we make can be used to inform subsequent choices and behavior. We investigated whether and how people with and without schizophrenia use positive and negative social outcomes and emotional displays to inform decisions to place trust in social partners. We also investigated the impact of reversals in social partners' behavior on decisions to trust. Thirty-two people with schizophrenia and 29 control participants completed a task in which they decided how much trust to place in social partners' showing either a dynamic emotional (smiling, scowling) or neutral display. Interactions were predetermined to result in positive (trust reciprocated) or negative (trust abused) outcomes, and we modeled changes in trust decisions over the course of repeated interactions. Compared to controls, people with schizophrenia were less sensitive to positive social outcomes in that they placed less trust in trustworthy social partners during initial interactions. By contrast, people with schizophrenia were more sensitive to negative social outcomes during initial interactions with untrustworthy social partners, placing less trust in these partners compared to controls. People with schizophrenia did not differ from controls in detecting social partner behavior reversals from trustworthy to untrustworthy; however, they had difficulties detecting reversals from untrustworthy to trustworthy. Importantly, decisions to trust were associated with real-world social functioning. We discuss the implications of these findings for understanding social engagement among people with schizophrenia and the development of psychosocial interventions for social functioning.

Comparative Autonomic Responses to Diagnostic Interviewing between Individuals with GAD, MDD, SAD and Healthy Controls

Dysregulation of the autonomic nervous system (ANS) has been well documented in individuals diagnosed with a range of psychological disorders, including generalized anxiety disorder (GAD) and major depressive disorder (MDD). Moreover, these disorders both confer an increased risk of cardiovascular disease-which may relate to increased sympathetic and decreased parasympathetic tone. Extant research has indicated a reduction in autonomic flexibility in GAD, and while reduced flexibility has also been seen in MDD, the specific physiological alterations have been more difficult to categorize due to methodological limitations, including high co-morbidity rates with anxiety disorders. Prior studies have largely assessed autonomic functioning in stress paradigms or at the trait level, yet to date, no research has investigated the ANS during a diagnostic interview, a ubiquitous task employed in both research and clinical settings. In this study we sought to identify physiological differences in both branches of the ANS across diagnostic categories in the context of a diagnostic interview. Participants (n = 82) were administered a structured clinical interview, during which heart rate (HR), respiratory sinus arrhythmia (RSA) and pre-ejection period (PEP) were recorded in participants carrying a diagnosis of GAD (n = 34), MDD (n = 22), Social Anxiety Disorder (SAD; n = 15) and healthy controls (n = 27). Person-specific linear regression models were employed to assess the level and slope for HR, RSA and PEP throughout the course of the interview. A multivariate analysis of variance (MANOVA) model was conducted to baseline differences in HR, RSA and PEP between diagnostic groups. Multiple regression models were then conducted to differences in slope of HR, RSA and PEP throughout the course of the interview amongst diagnostic groups, including both suppression and worry as moderators. Results indicated significant increases in RSA throughout the interview in MDD (p = 0.01) compared to healthy controls. Worry itself was found to be a more significant predictor of both decreased PEP (p = 0.02) and increased HR (p = 0.05). Suppression exhibited a dampening effect on individuals with worry and GAD, whereby those who suppressed had dampened HR responsiveness compared to those who did not suppress. These findings are consistent with existing literature supporting a decreased autonomic flexibility in certain psychological disorders, as well as indicate distinct physiological differences across certain transdiagnostic features of mood and anxiety disorders.

Interpersonal Problems Predict Differential Response to Cognitive Versus Behavioral Treatment in a Randomized Controlled Trial

OBJECTIVE

We examined dimensional interpersonal problems as moderators of cognitive behavioral therapy (CBT) versus its components (cognitive therapy [CT] and behavioral therapy [BT]). We predicted that people with generalized anxiety disorder (GAD) whose interpersonal problems reflected more dominance and intrusiveness would respond best to a relaxation-based BT compared to CT or CBT, based on studies showing that people with personality features associated with a need for autonomy respond best to treatments that are more experiential, concrete, and self-directed compared to therapies involving abstract analysis of one's problems (e.g., containing CT).

METHOD

This was a secondary analysis of Borkovec, Newman, Pincus, and Lytle (2002). Forty-seven participants with principal diagnoses of GAD were assigned randomly to combined CBT (n = 16), CT (n = 15), or BT (n = 16).

RESULTS

As predicted, compared to participants with less intrusiveness, those with dimensionally more intrusiveness responded with greater GAD symptom reduction to BT than to CBT at posttreatment and greater change to BT than to CT or CBT across all follow-up points. Similarly, those with more dominance responded better to BT compared to CT and CBT at all follow-up points. Additionally, being overly nurturant at baseline was associated with GAD symptoms at baseline, post, and all follow-up time-points regardless of therapy condition.

CONCLUSIONS

Generally anxious individuals with domineering and intrusive problems associated with higher need for control may respond better to experiential behavioral interventions than to cognitive interventions, which may be perceived as a direct challenge of their perceptions.

Differential responses of positive affect, negative affect, and worry in CBT for generalized anxiety disorder: A person-specific analysis of symptom course during therapy

INTRODUCTION

Research indicates that individuals with generalized anxiety disorder (GAD) may experience deficits in positive affect (PA), and tend to dampen or intentionally suppress PA. Despite the presence of PA-related pathology in GAD, little is known about change in PA during GAD treatment.

OBJECTIVE

This study examines changes in PA, negative affect (NA) and worry in seven participants during cognitive behavioral therapy (CBT) for GAD.

METHOD

Intensive repeated measures (i.e., time series) data were subjected to person-specific regression analysis to delineate individual change trajectories.

RESULTS

Significant improvement in worry was observed in all but one participant. Fear and irritability - indices of NA - each improved in 5/7 participants while sadness improved in 4/7 participants (worsening in one). Of all symptom domains, PA had the poorest treatment response: PA improved in only 2/7 participants and actually significantly worsened in 5/7 individuals even as NA and worry improved during therapy.

CONCLUSION

These findings indicate that treatment gains from traditional CBT for GAD may not generalize to improvements in PA regulation, or even emotional functioning more broadly. This evidence is a call to increase the focus on PA regulation in treatment for GAD; perhaps PA could be a missing piece in our understanding of ways to bolster GAD treatment outcomes.

The addition of depression to the Framingham Risk Equation model for predicting coronary heart disease risk in women

BACKGROUND

Depression is widely considered to be an independent and robust predictor of Coronary Heart Disease (CHD), however is seldom considered in the context of formal risk assessment. We assessed whether the addition of depression to the Framingham Risk Equation (FRE) improved accuracy for predicting 10-year CHD in a sample of women.

DESIGN

A prospective, longitudinal design comprising an age-stratified, population-based sample of Australian women collected between 1993 and 2011 (n=862).

METHODS

Clinical depressive disorder was assessed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID-I/NP), using retrospective age-of-onset data. A composite measure of CHD included non-fatal myocardial infarction, unstable angina coronary intervention or cardiac death. Cox proportional-hazards regression models were conducted and overall accuracy assessed using area under receiver operating characteristic (ROC) curve analysis.

RESULTS

ROC curve analyses revealed that the addition of baseline depression status to the FRE model improved its overall accuracy (AUC:0.77, Specificity:0.70, Sensitivity:0.75) when compared to the original FRE model (AUC:0.75, Specificity:0.73, Sensitivity:0.67). However, when calibrated against the original model, the predicted number of events generated by the augmented version marginally over-estimated the true number observed.

CONCLUSIONS

The addition of a depression variable to the FRE equation improves the overall accuracy of the model for predicting 10-year CHD events in women, however may over-estimate the number of events that actually occur. This model now requires validation in larger samples as it could form a new CHD risk equation for women.

Depression is a risk factor for incident coronary heart disease in women: An 18-year longitudinal study

BACKGROUND

According to a recent position paper by the American Heart Association, it remains unclear whether depression is a risk factor for incident Coronary Heart Disease (CHD). We assessed whether a depressive disorder independently predicts 18-year incident CHD in women.

METHOD

A prospective longitudinal study of 860 women enrolled in the Geelong Osteoporosis Study (1993-2011) was conducted. Participants were derived from an age-stratified, representative sample of women (20-94 years) randomly selected from electoral rolls in South-Eastern Australia. The exposure was a diagnosis of a depressive disorder using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders. Outcomes data were collected from hospital medical records: (1) PRIMARY OUTCOME: a composite measure of cardiac death, non-fatal Myocardial Infarction or coronary intervention. (2) Secondary outcome: any cardiac event (un/stable angina, cardiac event not otherwise defined) occurring over the study period.

RESULTS

Seven participants were excluded based on CHD history. Eighty-three participants (9.6%) recorded ≥1 cardiac event over the study period; 47 had a diagnosis that met criteria for inclusion in the primary analysis. Baseline depression predicted 18-year incidence, adjusting for (1) anxiety (adj. OR:2.39; 95% CIs:1.19-4.82), plus (2) typical risk factors (adj. OR:3.22; 95% CIs:1.45-6.93), plus (3) atypical risk factors (adj. OR:3.28; 95% CIs:1.36-7.90). This relationship held when including all cardiac events. No relationship was observed between depression and recurrent cardiac events.

CONCLUSION

The results of this study support the contention that depression is an independent risk factor for CHD incidence in women. Moreover, the strength of association between depression and CHD incidence was of a greater magnitude than any typical and atypical risk factor.

Reductions in the diurnal rigidity of anxiety predict treatment outcome in cognitive behavioral therapy for generalized anxiety disorder

Generalized anxiety disorder (GAD) is a chronic and disabling disorder which is characterized by worrisome mentation about future outcomes. Because the evocative stimuli in GAD are largely internally derived, the feared outcomes contained in worry episodes can be invoked--and responded to--regardless of external context. We hypothesized that individuals with GAD would be entrained to internally-regulated, fixed patterns of anxiety on a day-to-day basis and that successful therapeutic intervention would serve to mitigate this entrainment. Thus, the present study examined the constructs of flexibility and rigidity as they apply to the daily fluctuation of anxious symptoms in individuals with GAD. We aimed to demonstrate that an apparently variable system can be conceptualized as rigid when the variability maps onto stable and predictable periodic oscillations. Sixty-nine individuals completed cognitive-behavioral treatment for GAD. Average age was 36.62 years (SD = 11.56), and participants were mostly Caucasian (89.5%) and female (68.4%). Daily-diary data indicating level of anxiety on a 0 to 100-point scale and collected four times per day were subjected to spectral analysis in order to determine the spectral power attributable to daily oscillations--which was related to the degree of rigidity in daily anxiety. Diurnal rigidity decreased throughout therapy and the degree to which rigidity was reduced significantly predicted reliable change at post-treatment. Thus, symptom rigidity can be conceptualized as stable periodic fluctuation and is discernible from other metrics of volatility in repeated measures data. Moreover, diurnal rigidity is significantly reduced during treatment, facilitating flexible responding to environmental demands.

Sympathetic and hypothalamic-pituitary-adrenal asymmetry in generalized anxiety disorder

Physiologic investigations of generalized anxiety disorder (GAD) have skewed toward assessment of the autonomic nervous system, largely neglecting hypothalamic-pituitary-adrenal (HPA) axis variables. Although these systems coordinate-suggesting a degree of symmetry-to promote adaptive functioning, most studies opt to monitor either one system or the other. Using a ratio of salivary alpha-amylase (sAA) over salivary cortisol, the present study examined symmetry between the sympathetic nervous system (SNS) and HPA axis in individuals with GAD (n = 71) and healthy controls (n = 37). Compared to healthy controls, individuals with GAD exhibited greater baseline ratios of sAA/cortisol and smaller ratios of sAA/cortisol following a mental arithmetic challenge. We propose that the present study provides evidence for SNS-HPA asymmetry in GAD. Further, these results suggest that increased SNS suppression in GAD may be partially mediated by cortisol activity.

Mood-Reactive Self-Esteem and Depression Vulnerability: Person-Specific Symptom Dynamics via Smart Phone Assessment

Cognitive theories of depression suggest that mood-reactive self-esteem, a pattern of cognitive reactivity where low self-esteem is temporally dependent on levels of sadness, represents vulnerability for depression. Few studies have directly tested this hypothesis, particularly using intensive data collection methods (i.e., experience sampling) required to capture the temporal dynamics of sadness and self-esteem as they unfold naturally, over time. In this study we used participants' smartphones to collect multiple daily ratings of sadness and self-esteem over three weeks, in the real world. We then applied dynamic factor modeling to explore theoretically driven hypotheses about the temporal dependency of self-esteem on sadness (i.e., mood-reactive self-esteem) and its relationship to indices of depression vulnerability both contemporaneously (e.g., rumination, sad mood persistence) and prospectively (e.g., future symptomatology). In sum, individuals who demonstrated mood-reactive self-esteem reported higher levels of rumination at baseline, more persistent sad mood over three weeks, and increased depression symptoms at the end of three weeks above and beyond a trait-like index of self-esteem. The integration of smartphone assessment and person-specific analytics employed in this study offers an exiting new avenue to advance the study and treatment of depression.

The self-rating of the effects of alcohol questionnaire predicts heavy episodic drinking in a high-risk eating disorder population

OBJECTIVE

Heavy episodic drinking (HED) is a serious problem among college women at high-risk for developing eating disorders (EDs). The main objectives of this study are to determine the relationship of the self-rating of the effects of alcohol (SRE) questionnaire and HED over time, and to determine the effects of relationship breakups on HED among college-aged women at high-risk for EDs.

METHOD

Data collected from 163 participants in a randomized controlled trial evaluating the effectiveness of an ED prevention program were used in the analyses. Measures included the SRE, obtained at baseline, and self-reports of the number of HED episodes and relationship breakups each month for the past 12 months.

RESULTS

Generalized linear mixed-effect regression models with Poisson distribution were conducted to test the effects of several variables on reported HED episodes over 12 months. Analyses demonstrated that SRE scores and the presence of a breakup predicted increased HED over time.

DISCUSSION

The SRE may be useful in identifying individuals at risk of or with EDs who are at increased risk of HED. Furthermore, relationship breakups predict HED. Findings from the current study could be used to inform clinical interventions for this population.