Network analyses of ecological momentary emotion and avoidance assessments before and after cognitive behavioral therapy for anxiety disorders.

Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.

Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study.

BACKGROUND: Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences. OBJECTIVE: This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers. METHODS: A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers. RESULTS: As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit. CONCLUSIONS: This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43931.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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

BACKGROUND AND OBJECTIVES: 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.

Identifying the presence and timing of discrete mood states prior to therapy.

The present study tested a novel, person-specific method for identifying discrete mood profiles from time-series data, and examined the degree to which these profiles could be predicted by lagged mood and anxiety variables and time-based variables, including trends (linear, quadratic, cubic), cycles (12-hr, 24-hr, and 7-day), day of the week, and time of day. We analyzed ambulatory data from 45 individuals with mood and anxiety disorders prior to therapy. Data were collected four-times-daily for at least 30 days. Latent profile analysis was applied person-by-person to discretize each individual's continuous multivariate time series of rumination, worry, fear, anger, irritability, anhedonia, hopelessness, depressed mood, and avoidance. That is, each time point was classified according to its unique blend of emotional states, and latent classes representing discrete mood profiles were identified for each participant. We found that the modal number of latent classes per person was three (mean = 3.04, median = 3), with a range of two to four classes. After splitting each individual's time series into random halves for training and testing, we used elastic net regularization to identify the temporal and lagged predictors of each mood profile's presence or absence in the training set. Prediction accuracy was evaluated in the testing set. Across 127 models, the average area under the curve was 0.77, with sensitivity of 0.81 and specificity of 0.75. Brier scores indicated an average prediction accuracy of 83%.