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.

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.

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.

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.

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.

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.

A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer.

In this article, we present a Bayesian hierarchical model for predicting a latent health state from longitudinal clinical measurements. Model development is motivated by the need to integrate multiple sources of data to improve clinical decisions about whether to remove or irradiate a patient's prostate cancer. Existing modeling approaches are extended to accommodate measurement error in cancer state determinations based on biopsied tissue, clinical measurements possibly not missing at random, and informative partial observation of the true state. The proposed model enables estimation of whether an individual's underlying prostate cancer is aggressive, requiring surgery and/or radiation, or indolent, permitting continued surveillance. These individualized predictions can then be communicated to clinicians and patients to inform decision-making. We demonstrate the model with data from a cohort of low-risk prostate cancer patients at Johns Hopkins University and assess predictive accuracy among a subset for whom true cancer state is observed. Simulation studies confirm model performance and explore the impact of adjusting for informative missingness on true state predictions. R code is provided in an online supplement and at http://github.com/rycoley/prediction-prostate-surveillance.

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.

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.

Validating the rapid responder construct within a practice research network.

OBJECTIVE: The present study was a replication and extension of prior work (Stulz, Lutz, Leach, Lucock, & Barkham, ) that identified multiple groups of clients in treatment with high-symptom severity and markedly different recovery trajectories (rapid/early response vs. little or no response). METHOD: Using data collected through repeated administrations of the Depression subscale of the Treatment Outcome Package (n = 147), growth mixture modeling was employed to determine whether clients fell into discrete groups of response trajectories during 15 sessions of psychotherapy. Additionally, logistic regressions were conducted to assess possible predictors of group membership. RESULTS: Three separate groups of treatment responders were identified: 2 high-symptom groups-rapid responders and nonresponders-and 1 low-symptom group of nonresponders. Elevated social conflict and suicidality predicted increased likelihood of membership in the high-symptom nonresponder group. Increased feelings of interpersonal hostility and better sexual functioning predicted increased likelihood of membership in the rapid responder group. CONCLUSION: Replication of earlier results provides further evidence for the usefulness of modeling change during psychotherapy using multiple trajectories. Predictors of group membership indicate the influence of functional impairment on recovery, and support the importance of multidimensional measurement of client problems.

Sleep, event appraisal, and affect: An ecological momentary assessment study.

BACKGROUND: Appraisal theory posits that emotions result from cognitive appraisals of events and situations. Experimental work suggests that sleep influences cognitive processes and event appraisal, which the present study examines in real life. Poor sleep influences brain regions involved in the appraisal-to-emotion process, and tired participants showed more conservative appraisal and reported less positive and more negative affect. In the present study, we tested whether sleep duration and/or quality predicted more pleasant event appraisal and whether sleep moderated the association between event appraisal and affect. METHODS: Participants (N = 892) from the general Dutch population reported thrice daily on event appraisal and various emotions for 30 days and once daily on sleep duration and quality. We constructed multilevel models to account for the nested structure of our data (observations within participants). RESULTS: Multilevel regression analyses showed that on days when participants reported having slept longer and better than their average, their event appraisal was more positive. Subjective sleep duration and quality did not influence the relationship between event appraisal and affect. Hence, poor sleep predicted changes in cognitive functioning, as people appraised situations as more unpleasant. LIMITATIONS: We measured subjective sleep duration and quality with two single items and focused on only pleasantness dimension of event appraisal. CONCLUSIONS: Results match perspectives on emotions as multicomponent systems involving appraisal processes. Understanding the elements of event appraisal may help unravel the detrimental effects of poor sleep on mental health and well-being.

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.

Evaluating the impact of depression, anxiety & autonomic function on health related quality of life, vocational functioning and health care utilisation in acute coronary syndrome patients: the ADVENT study protocol.

BACKGROUND: Depression and anxiety are highly prevalent and co-morbid in acute coronary syndrome patients. Somatic and cognitive subtypes of depression and anxiety in acute coronary syndrome have been shown to be associated with mortality although their association with patient outcomes is unknown, as are the mechanisms that underpin these associations. We are conducting a prospective cohort study which aims to examine in acute coronary syndrome patients: (1) the role of somatic subtypes of depression and anxiety as predictors of health related quality of life outcomes; (2) how somatic subtypes of depression and anxiety relate to long term vocational functioning and healthcare utilisation; and (3) the role of the autonomic nervous system assessed by heart rate variability as a moderator of these associations. METHODS: Patients are being screened after index admission for acute coronary syndrome at a single, high volume centre, MonashHeart, Monash Health, Victoria, Australia. The inclusion criterion is all patients aged > 21 years old and fluent in English admitted to MonashHeart, Monash Health with a diagnosis of acute coronary syndrome. The primary outcome is mean health related quality of life (Short Form-36) Physical and Mental Health Summary scores at 12 and 24 months in subtypes with somatic symptoms of depression and anxiety. Depressive domains are assessed by the Beck Depression Inventory II and the Cardiac Depression Scale. Anxiety is measured using the Speilberger State-Trait Anxiety Inventory and the Crown Crisp Phobic Anxiety questionnaire. Secondary outcomes include clinical variables, healthcare service utilisation and vocational functioning. DISCUSSION: This manuscript presents the protocol for a prospective cohort study which will investigate the role of somatic subtypes of depression and anxiety as predictors of health related quality of life, long-term vocational functioning and health service use, and the role of the autonomic nervous system in moderating these associations. Findings from the study have the potential to inform more effective pharmacological, psychological and behavioural interventions and better guide health policy on the use of health care resources.

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.

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.

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.

Unsupervised clustering of autonomic temporal networks in clinically distressed and psychologically healthy individuals.

The present study recruited psychologically healthy individuals and individuals with clinically-severe Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses, including generalized anxiety disorder, major depressive disorder, social anxiety disorder, posttraumatic stress disorder, panic disorder, persistent depressive disorder, and specific phobia. During the course of a structured clinical interview, 200 individuals provided continuous electrocardiogram and impedance cardiography data. Of these N = 150 were used for exploratory analyses and N = 50 for confirmatory analyses. From these time series, we modeled heart period (i.e. interbeat interval), pre-ejection period, respiratory sinus arrhythmia, and respiration rate. The group iterative multiple model estimation (GIMME) model was used to generate group and individual-level network models which, in turn, were used to conduct unsupervised classification of individual-level models into subgroups. Four subgroups were identified, comprising N = 22, N = 25, N = 26, and N = 61 individuals, with an additional 16 individuals left unclassified. The subgroup models were then used to estimate directed network models, from which out-degree and in-degree centrality were estimated for each group. Two groups, Group 2 and Group 4 exhibited elevated symptoms of depression and anxiety relative to the remaining sample. However, only one of these, Group 2, exhibited additional physiological risk features, including a significantly elevated average heart rate, and significantly reduced parasympathetic regulation (measured via respiratory sinus arrhythmia). We discuss the implications for utilizing network models for conducting systems-level analyses of physiological systems in clinically-distressed and psychologically healthy individuals.