Mixture multilevel vector-autoregressive modeling.

With the rising popularity of intensive longitudinal research, the modeling techniques for such data are increasingly focused on individual differences. Here we present mixture multilevel vector-autoregressive modeling, which extends multilevel vector-autoregressive modeling by including a mixture, to identify individuals with similar traits and dynamic processes. This exploratory model identifies mixture components, where each component refers to individuals with similarities in means (expressing traits), autoregressions, and cross-regressions (expressing dynamics), while allowing for some interindividual differences in these attributes. Key issues in modeling are discussed, where the issue of centering predictors is examined in a small simulation study. The proposed model is validated in a simulation study and used to analyze the affective data from the COGITO study. These data consist of samples for two different age groups of over 100 individuals each who were measured for about 100 days. We demonstrate the advantage of exploratory identifying mixture components by analyzing these heterogeneous samples jointly. The model identifies three distinct components, and we provide an interpretation for each component motivated by developmental psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The dynamics of social activation and suspiciousness in individuals at ultra-high risk for psychosis.

INTRODUCTION: Social functioning is often impaired during the ultra-high risk (UHR) phase for psychosis, but group-level studies regarding the role of social functioning in transition to psychosis are inconsistent. Exploring the inter-individual differences which underlie the association between social functioning and psychotic symptoms in this phase could yield new insights. OBJECTIVE: To examine the idiographic and dynamic association between social activation and suspiciousness in individuals at UHR for psychosis using time-series analysis. METHODS: Twenty individuals at UHR for psychosis completed a diary application every evening for 90 days. Two items on social activation (quantity: 'time spent alone' and quality: 'feeling supported') and two items on suspiciousness ('feeling suspicious' and 'feeling disliked') were used. Time series (T = 90) of each individual were analyzed using vector auto regression analysis (VAR), to estimate the lagged (over 1 day) effect of social activation on suspiciousness, and vice versa, as well as their contemporaneous associations. RESULTS: Heterogeneous person-specific associations between social activation and suspiciousness were found in terms of strength, direction and temporal aspects. CONCLUSIONS: The association between social activation and suspiciousness differs amongst individuals who are at UHR for psychosis. These findings underline the importance of tailoring psychosocial interventions to the individual. Future studies may examine whether using results of single-subject studies in clinical practice to personalize treatment goals leads to better treatment outcomes.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people.

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

A comprehensive model framework for between-individual differences in longitudinal data.

Across different fields of research, the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual level, our model framework accounts for various attributes of longitudinal data, such as growth and decline, cyclical trends, and the dynamic interplay between variables over time. At the between-individual level, our framework contains continuous and categorical latent variables to account for between-individual differences. This framework encompasses several well-known longitudinal models, including multilevel regression models, growth curve models, growth mixture models, vector-autoregressive models, and multilevel vector-autoregressive models. The general model framework is specified and its key characteristics are illustrated using famous longitudinal models as concrete examples. Various longitudinal models are reviewed and it is shown that all these models can be united into our comprehensive model framework. Extensions to the model framework are discussed. Recommendations for selecting and specifying longitudinal models are made for empirical researchers who aim to account for between-individual differences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Visualizing Uncertainty to Promote Clinicians' Understanding of Measurement Error.

Measurement error is an inherent part of any test score. This uncertainty is generally communicated in ways that can be difficult to understand for clinical practitioners. In this empirical study, we evaluate the impact of several communication formats on the interpretation of measurement accuracy and its influence on the decision-making process in clinical practice. We provided 230 clinical practitioners with score reports in five formats: textual, error bar, violin plot, diamond plot, and quantile dot plot. We found that quantile dot plots significantly increased accuracy in the assessment of measurement uncertainty compared with other formats. However, a direct relation between visualization format and decision quality could not be found. Although traditional confidence intervals and error bars were favored by many participants due to their familiarity, responses revealed several misconceptions that make the suitability of these formats for communicating uncertainty questionable. Our results indicate that new visualization formats can successfully reduce errors in interpretation.

Regaining control of your emotions? Investigating the mechanisms underlying effects of cognitive control training for remitted depressed patients.

Studies suggest that cognitive control training shows potential as a preventive intervention for depression. At the same time, little is known regarding the mechanisms underlying effects of cognitive control training. Informed by theoretical frameworks of cognitive risk for recurrent depression (De Raedt & Koster, 2010; Siegle et al., 2007), the current study sought to model direct effects of cognitive control training on the complex interplay between affect, emotion regulation, residual symptomatology, and resilience in a sample of remitted depressed patients (n = 92). Combining a 4-week experience sampling procedure with an experimental manipulation of cognitive control, we observed beneficial effects of cognitive control training on deployment of rumination. In addition, we obtained evidence for the causal involvement of cognitive control in efficacy of emotion regulation. In contrast to our expectations, cognitive control training did not exert immediate effects on residual symptomatology or resilience when compared with an active control condition, nor did cognitive control training impact the complex interplay between these variables. Overall, immediate effects of cognitive control training on functioning in daily life were limited. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Network Approach to Trauma, Dissociative Symptoms, and Psychosis Symptoms in Schizophrenia Spectrum Disorders.

BACKGROUND: Dissociative experiences commonly occur in schizophrenia spectrum disorders (SSD). Yet little is known about how dissociative experiences in SSD are related to SSD symptoms. Accordingly, we investigated the relations between dissociative experiences and SSD symptoms, focusing on symptoms bridging these 2 symptom clusters as well as their relation to reported trauma history. STUDY DESIGN: Network analyses were conducted on the responses of 248 individuals with an SSD who enrolled from multiple mental health centers in The Netherlands. Dissociative experiences were assessed via the Dissociative Experience Scale, SSD symptoms using the Positive and Negative Syndrome Scale, and trauma history through the Trauma History Questionnaire. STUDY RESULTS: The results indicated that dissociative symptoms in SSD are mostly independent of other symptoms, but that emotional distress bridges between the dissociative and SSD symptom clusters. Furthermore, results revealed associations between positive and negative SSD symptoms and trauma through emotional distress, whereas dissociative symptoms remained relatively isolated. CONCLUSION: Because SSD symptoms and dissociative experiences clustered relatively independent from each other, our findings promote the idea of tailored treatment approaches for individuals with an SSD with frequent dissociative experiences, specifically targeting these symptoms.

Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT.

INTRODUCTION: Youth in remission of depression or anxiety have high risks of relapse. Relapse prevention interventions may prevent chronicity. Aim of the study is therefore to (1) examine efficacy of the personalised StayFine app for remitted youth and (2) identify high-risk groups for relapse and resilience. METHOD AND ANALYSIS: In this Dutch single-blind parallel-group randomised controlled trial, efficacy of app-based monitoring combined with guided app-based personalised StayFine intervention modules is assessed compared with monitoring only. In both conditions, care as usual is allowed. StayFine modules plus monitoring is hypothesised to be superior to monitoring only in preventing relapse over 36 months. Participants (N=254) are 13-21 years and in remission of depression or anxiety for >2 months. Randomisation (1:1) is stratified by previous treatment (no treatment vs treatment) and previous episodes (1, 2 or >3 episodes). Assessments include diagnostic interviews, online questionnaires and monitoring (ecological momentary assessment with optional wearable) after 0, 4, 12, 24 and 36 months. The StayFine modules are guided by certified experts by experience and based on preventive cognitive therapy and ingredients of cognitive behavioural therapy. Personalisation is based on shared decision-making informed by baseline assessments and individual symptom networks. Time to relapse (primary outcome) is assessed by the Kiddie Schedule for Affective Disorders and Schizophrenia-lifetime version diagnostic interview. Intention-to-treat survival analyses will be used to examine the data. Secondary outcomes are symptoms of depression and anxiety, number and duration of relapses, global functioning, and quality of life. Mediators and moderators will be explored. Exploratory endpoints are monitoring and wearable outcomes. ETHICS, FUNDING AND DISSEMINATION: The study was approved by METC Utrecht and is funded by the Netherlands Organisation for Health Research and Development (636310007). Results will be submitted to peer-reviewed scientific journals and presented at (inter)national conferences. TRIAL REGISTRATION NUMBER: NCT05551468; NL8237.

Misleading graphs in context: Less misleading than expected.

Misleading graphs are a source of misinformation that worry many experts. Especially people with a low graph literacy are thought to be persuaded by graphs that misrepresent the underlying data. But we know little about how people interpret misleading graphs and how these graphs influence their opinions. In this study we focus on the effect of truncating the y-axis for a line chart which exaggerates an upgoing trend. In a randomized controlled trial, we showed participants either a normal or a misleading chart, and we did so in two different contexts. After they had seen the graphs, we asked participants their opinion on the trend and to give an estimation of the increase. Finally we measured their graph literacy. Our results show that context is the only significant factor in opinion-forming; the misleading graph and graph literacy had no effect. None of these factors had a significant impact on estimations for the increase. These results show that people might be less susceptible to misleading graphs than we thought and that context has more impact than a misleading y-axis.

Mindful yoga intervention as add-on to treatment as usual for young women with major depressive disorder: Results from a randomized controlled trial.

OBJECTIVE: To examine the added value of a 9-week mindful yoga intervention (MYI) as add-on to treatment as usual (TAU) in reducing depression for young women (18-34 years) with major depressive disorder (MDD). METHOD: Randomized controlled trial (RCT; n = 171) comparing TAU + MYI with TAU-only. Assessments were at baseline, postintervention, and at 6- and 12-month follow-up. Primary outcome measures were clinician-rated and self-reported symptoms of depression, together with a diagnostic interview to establish MDD diagnosis that was restricted to the baseline and 12-month follow-up assessments. Quality of life in various domains was assessed as secondary outcome measure. As potential mediators for treatment efficacy, we included self-report measures of rumination, self-criticism, self-compassion, intolerance of uncertainty, perceived body awareness and dispositional mindfulness, together with behavioral measures of attentional bias (AB) and depression-related self-associations. RESULTS: Adding MYI to TAU did not lead to greater reduction of depression symptoms, lower rate of MDD diagnosis or increase in quality of life in various domains of functioning at post and follow-up assessments. There were no indirect effects through any of the potential mediators, with the exception of self-compassion. CONCLUSION: Adding MYI to TAU appeared not more efficacious than TAU-only in reducing depression symptoms in young women. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Consensus-based guidance for conducting and reporting multi-analyst studies.

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

A cross-sectional study of lactation room quality and Dutch working mothers' satisfaction, perceived ease of, and perceived support for breast milk expression at work.

BACKGROUND: The challenge of combining professional work and breastfeeding is a key reason why women choose not to breastfeed or to stop breastfeeding early. We posited that having access to a high-quality lactation room at the workplace could influence working mothers' satisfaction and perceptions related to expressing breast milk at work, which could have important longer term consequences for the duration of breastfeeding. Specifically, we aimed to (1) develop a checklist for assessing the quality of lactation rooms and (2) explore how lactation room quality affects lactating mothers' satisfaction and perceptions. Drawing on social ecological insights, we hypothesized that the quality of lactation rooms (operationalized as any space used for expressing milk at work) would be positively related to mothers' satisfaction with the room, perceived ease of, and perceived support for milk expression at work. METHODS: We conducted two studies. In Study 1 we developed a lactation room quality checklist (LRQC) and assessed its reliability twice, using samples of 33 lactation rooms (Study 1a) and 31 lactation rooms (Study 1b). Data were collected in the Northern part of the Netherlands (between December 2016 and April 2017). Study 2 comprised a cross-sectional survey of 511 lactating mothers, working in a variety of Dutch organizations. The mothers were recruited through the Facebook page of a popular Dutch breastfeeding website. They completed online questionnaires containing the LRQC and measures aimed at assessing their satisfaction and perceptions related to milk expression at work (in June and July 2017). RESULTS: The LRQC was deemed reliable and easy to apply in practice. As predicted, we found that objectively assessed higher-quality lactation rooms were associated with increased levels of satisfaction with the lactation rooms, perceived ease of milk expression at work, and perceived support from supervisors and co-workers for expressing milk in the workplace. CONCLUSIONS: The availability of a high-quality lactation room could influence mothers' decisions regarding breast milk expression at work and the commencement and/or continuation of breastfeeding. Future studies should explore whether and how lactation room quality affects breastfeeding choices, and which aspects are most important to include in lactation rooms.

Insight Into Individual Differences in Emotion Dynamics With Clustering.

Studying emotion dynamics through time series models is becoming increasingly popular in the social sciences. Across individuals, dynamics can be rather heterogeneous. To enable comparisons and generalizations of dynamics across groups of individuals, one needs sophisticated tools that express the essential similarities and differences. A way to proceed is to identify subgroups of people who are characterized by qualitatively similar emotion dynamics through dynamic clustering. So far, these methods assume equal generating processes for individuals per cluster. To avoid this overly restrictive assumption, we outline a probabilistic clustering approach based on a mixture model that clusters on individuals' vector autoregressive coefficients. We evaluate the performance of the method and compare it with a nonprobabilistic method in a simulation study. The usefulness of the methods is illustrated using 366 ecological momentary assessment time series with external measures of depression and anxiety.

A tutorial on regression-based norming of psychological tests with GAMLSS.

A norm-referenced score expresses the position of an individual test taker in the reference population, thereby enabling a proper interpretation of the test score. Such normed scores are derived from test scores obtained from a sample of the reference population. Typically, multiple reference populations exist for a test, namely when the norm-referenced scores depend on individual characteristic(s), as age (and sex). To derive normed scores, regression-based norming has gained large popularity. The advantages of this method over traditional norming are its flexible nature, yielding potentially more realistic norms, and its efficiency, requiring potentially smaller sample sizes to achieve the same precision. In this tutorial, we introduce the reader to regression-based norming, using the generalized additive models for location, scale, and shape (GAMLSS). This approach has been useful in norm estimation of various psychological tests. We discuss the rationale of regression-based norming, theoretical properties of GAMLSS and their relationships to other regression-based norming models. Based on 6 steps, we describe how to: (a) design a normative study to gather proper normative sample data; (b) select a proper GAMLSS model for an empirical scale; (c) derive the desired normed scores for the scale from the fitted model, including those for a composite scale; and (d) visualize the results to achieve insight into the properties of the scale. Following these steps yields regression-based norms with GAMLSS for a psychological test, as we illustrate with normative data of the intelligence test IDS-2. The complete R code and data set is provided as online supplemental material. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Bias-Variance Trade-Off in Continuous Test Norming.

In continuous test norming, the test score distribution is estimated as a continuous function of predictor(s). A flexible approach for norm estimation is the use of generalized additive models for location, scale, and shape. It is unknown how sensitive their estimates are to model flexibility and sample size. Generally, a flexible model that fits at the population level has smaller bias than its restricted nonfitting version, yet it has larger sampling variability. We investigated how model flexibility relates to bias, variance, and total variability in estimates of normalized z scores under empirically relevant conditions, involving the skew Student t and normal distributions as population distributions. We considered both transversal and longitudinal assumption violations. We found that models with too strict distributional assumptions yield biased estimates, whereas too flexible models yield increased variance. The skew Student t distribution, unlike the Box-Cox Power Exponential distribution, appeared problematic to estimate for normally distributed data. Recommendations for empirical norming practice are provided.

Bayesian Gaussian distributional regression models for more efficient norm estimation.

A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost-efficient high-quality norms. The method is illustrated using empirical normative data from the IDS-2 intelligence test.

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

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

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.

A multiverse analysis of early attempts to replicate memory suppression with the Think/No-think Task.

In 2001, Anderson and Green [2001. Suppressing unwanted memories by executive control. Nature, 410(6826), 366-369] showed memory suppression using a novel Think/No-think (TNT) task. When participants attempted to prevent studied words from entering awareness, they reported fewer of those words than baseline words in subsequent cued recall (i.e., suppression effect). The TNT literature contains predominantly positive findings and few null-results. Therefore we report unpublished replications conducted in the 2000s (N = 49; N = 36). As the features of the data obtained with the TNT task call for a variety of plausible solutions, we report parallel "universes" of data-analyses (i.e., multiverse analysis) testing the suppression effect. Two published studies (Wessel et al., 2005. Dissociation and memory suppression: A comparison of high and low dissociative individuals' performance on the Think-No think Task. Personality and Individual Differences, 39(8), 1461-1470, N = 68; Wessel et al., 2010. Cognitive control and suppression of memories of an emotional film. Journal of Behavior Therapy and Experimental Psychiatry, 41(2), 83-89. https://doi.org/10.1016/j.jbtep.2009.10.005, N = 80) were reanalysed in a similar fashion. For recall probed with studied cues (Same Probes, SP), some tests (sample 3) or all (samples 2 and 4) showed statistically significant suppression effects, whereas in sample 1, only one test showed significance. Recall probed with novel cues (Independent Probes, IP) predominantly rendered non-significant results. The absence of statistically significant IP suppression effects raises problems for inhibition theory and its implication that repression is a viable mechanism of forgetting. The pre-registration, materials, data, and code are publicly available (https://osf.io/qgcy5/).

Model Selection in Continuous Test Norming With GAMLSS.

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.