Considering the 'With Whom': Differences Between Event- and Signal-Contingent ESM Data of Person-Specific Social Interactions.

Experience sampling studies often aim to capture social interactions. A central methodological question in such studies is whether to use event- or signal-contingent sampling. The little existing research on this issue has not taken into account that social interactions occur with unique interaction partners (e.g., Anna or Tom). We analyze one week of social interaction data of 286 students from the University of Pittsburgh (60.8% male, mean age 19.2 years), taking into account the unique interaction partners of each student. Specifically, we investigate the differences between event- and signal contingent sampling in (1) the total number of unique interaction partners captured, as well as (2) the kinds of relationships, and (3) the quality of social interactions with these captured interaction partners. Apart from a larger quantity of interactions and unique interaction partners in the event-contingent sampling design, our analyses indicate subtle differences between the two designs when aiming to assess social interactions with more distant interaction partners, such as coworkers or strangers. Most importantly, in our analyses, specific interaction partners and social roles explained a considerable amount of variance in the quality of social interactions (up to 20.5%), suggesting that future research would benefit greatly from considering "with whom" individuals interact.

Assessment of fit of the time-varying dynamic partial credit model using the posterior predictive model checking method.

Several new models based on item response theory have recently been suggested to analyse intensive longitudinal data. One of these new models is the time-varying dynamic partial credit model (TV-DPCM; Castro-Alvarez et al., Multivariate Behavioral Research, 2023, 1), which is a combination of the partial credit model and the time-varying autoregressive model. The model allows the study of the psychometric properties of the items and the modelling of nonlinear trends at the latent state level. However, there is a severe lack of tools to assess the fit of the TV-DPCM. In this paper, we propose and develop several test statistics and discrepancy measures based on the posterior predictive model checking (PPMC) method (PPMC; Rubin, The Annals of Statistics, 1984, 12, 1151) to assess the fit of the TV-DPCM. Simulated and empirical data are used to study the performance of and illustrate the effectiveness of the PPMC method.

Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.

Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.

A Time-Varying Dynamic Partial Credit Model to Analyze Polytomous and Multivariate Time Series Data.

The accessibility to electronic devices and the novel statistical methodologies available have allowed researchers to comprehend psychological processes at the individual level. However, there are still great challenges to overcome as, in many cases, collected data are more complex than the available models are able to handle. For example, most methods assume that the variables in the time series are measured on an interval scale, which is not the case when Likert-scale items were used. Ignoring the scale of the variables can be problematic and bias the results. Additionally, most methods also assume that the time series are stationary, which is rarely the case. To tackle these disadvantages, we propose a model that combines the partial credit model (PCM) of the item response theory framework and the time-varying autoregressive model (TV-AR), which is a popular model used to study psychological dynamics. The proposed model is referred to as the time-varying dynamic partial credit model (TV-DPCM), which allows to appropriately analyze multivariate polytomous data and nonstationary time series. We test the performance and accuracy of the TV-DPCM in a simulation study. Lastly, by means of an example, we show how to fit the model to empirical data and interpret the results.

The Migration Experience: A Conceptual Framework and Systematic Scoping Review of Psychological Acculturation.

ACADEMIC ABSTRACT: One of the key challenges to researching psychological acculturation is the immense heterogeneity in theories and measures. These inconsistencies make it difficult to compare past literature, hinder straightforward measurement selections, and stifle theoretical integration. To structure acculturation, we propose to utilize the four basic aspects of human experiences (wanting, feeling, thinking, and doing) as a conceptual framework. We use this framework to build a theory-driven assessment of past theoretical (final N = 92), psychometric (final N = 233), and empirical literature (final N = 530). We find that the framework allows us to examine and compare past conceptualizations. For example, empirical works have understudied the more internal aspects of acculturation (i.e., motivations and feelings) compared with theoretical works. We, then, discuss the framework's novel insights including its temporal resolution, its comprehensive and cross-cultural structure, and how the framework can aid transparent and functional theories, studies, and interventions going forward. PUBLIC ABSTRACT: This systematic scoping review indicates that the concept of psychological acculturation can be structured in terms of affect (e.g., feeling at home), behavior (e.g., language use), cognition (e.g., ethnic identification), and desire (e.g., independence wish). We find that the framework is useful in structuring past research and helps with new predictions and interventions. We, for example, find a crucial disconnect between theory and practice, which will need to be resolved in the future.

Studying Daily Social Interaction Quantity and Quality in Relation to Depression Change: A Multi-Phase Experience Sampling Study.

Day-to-day social life and mental health are intertwined. Yet, no study to date has assessed how the quantity and quality of social interactions in daily life are associated with changes in depressive symptoms. This study examines these links using multiple-timescale data (iSHAIB data set; N = 133), where the level of depressive symptoms was measured before and after three 21-day periods of event-contingent experience sampling of individuals' interpersonal interactions (T = 64,112). We find weak between-person effects for interaction quantity and perceiving interpersonal warmth of others on changes in depressive symptoms over the 21-day period, but strong and robust evidence for overwarming-a novel construct representing the self-perceived difference between one's own and interaction partner's level of interpersonal warmth. The findings highlight the important role qualitative aspects of social interactions may play in the progression of individuals' depressive symptoms.

Need Fulfillment During Intergroup Contact: Three Experience Sampling Studies.

One challenge of modern intergroup contact research has been the question of when and why an interaction is perceived as positive and improves intergroup relations. We propose to consider the perceived fulfillment of the situationally most relevant need. We conducted three intensive longitudinal studies with recent migrants to capture their interactions with the majority out-group (Nmeasurements = 10,297; Nparticipants = 207). The situational need fulfillment mechanism is consistently a strong predictor of perceived interaction quality and positive out-group attitudes following intergroup interactions. The model is specific to out-group contact, robust to various need types, and works at least as well as Allport's contact conditions. As one of the first studies to test intergroup contact theory using intensive longitudinal data, we offer insight into the mechanisms of positive intergroup contact during real-life interactions and find situational motivations to be a key building block for understanding and addressing positive intergroup interactions.Public significance statement: In this article, we provide evidence that the fulfillment of situational needs during real-life intergroup contacts meaningfully predicts perceived interaction quality and positive outgroup attitudes. Methodologically, this offers a testament to the emerging practice of capturing real-life interactions using intensive longitudinal data. Theoretically, our results give weight to motivational fulfillment as a flexible and effective mechanism for understanding positive intergroup contact.

Modeling categorical time-to-event data: The example of social interaction dynamics captured with event-contingent experience sampling methods.

The depth of information collected in participants' daily lives with active (e.g., experience sampling surveys) and passive (e.g., smartphone sensors) ambulatory measurement methods is immense. When measuring participants' behaviors in daily life, the timing of particular events-such as social interactions-is often recorded. These data facilitate the investigation of new types of research questions about the timing of those events, including whether individuals' affective state is associated with the rate of social interactions (binary event occurrence) and what types of social interactions are likely to occur (multicategory event occurrences, e.g., interactions with friends or family). Although survival analysis methods have been used to analyze time-to-event data in longitudinal settings for several decades, these methods have not yet been incorporated into ambulatory assessment research. This article illustrates how multilevel and multistate survival analysis methods can be used to model the social interaction dynamics captured in intensive longitudinal data, specifically when individuals exhibit particular categories of behavior. We provide an introduction to these models and a tutorial on how the timing and type of social interactions can be modeled using the R statistical programming language. Using event-contingent reports (N = 150, Nevents = 64,112) obtained in an ambulatory study of interpersonal interactions, we further exemplify an empirical application case. In sum, this article demonstrates how survival models can advance the understanding of (social interaction) dynamics that unfold in daily life. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Feedback About a Person's Social Context - Personal Networks and Daily Social Interactions.

The social context of a person, meaning their social relationships and daily social interactions, is an important factor for understanding their mental health. However, personalised feedback approaches to psychotherapy do not consider this factor sufficiently yet. Therefore, we developed an interactive feedback prototype focusing specifically on a person's social relationships as captured with personal social networks (PSN) and daily social interactions as captured with experience sampling methodology (ESM). We describe the development of the prototype as well as two evaluation studies: Semi-structured interviews with students (N = 23) and a focus group discussion with five psychotherapy patients. Participants from both studies considered the prototype useful. The students considered participation in our study, which included social context assessment via PSN and ESM as well as a feedback session, insightful. However, it remains unclear how much insight the feedback procedure generated for the students beyond the insights they already gained from the assessments. The focus group patients indicated that in a clinical context, (social context) feedback may be especially useful to generate insight for the clinician and facilitate collaboration between patient and clinician. Furthermore, it became clear that the current feedback prototype requires explanations by a researcher or trained clinician and cannot function as a stand-alone intervention. As such, we discuss our feedback prototype as a starting point for future research and clinical implementation.

Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review.

BACKGROUND: Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people's social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people's social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. OBJECTIVE: Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. METHODS: We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. CONCLUSIONS: We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants' burden and form a holistic perspective on the social environment.

Transitions in depression: if, how, and when depressive symptoms return during and after discontinuing antidepressants.

PURPOSE: The aim of the current study is to provide insight into if, how, and when meaningful changes occur in individual patients who discontinue antidepressant medication. Agreement between macro-level quantitative symptom data, qualitative ratings, and micro-level Ecological Momentary Assessments is examined. METHODS: During and shortly after antidepressant discontinuation, depressive symptoms and 'feeling down' were measured in 56 participants, using the SCL-90 depression subscale weekly (macro-level) for 6 months, and 5 Ecological Momentary Assessments daily (micro-level) for 4 months (30.404 quantitative measurements in total). Qualitative information was also obtained, providing additional information to verify that changes were clinically meaningful. RESULTS: At the macro-level, an increase in depressive symptoms was found in 58.9% of participants that (a) was statistically reliable, (b) persisted for 3 weeks and/or required intervention, and (c) was clinically meaningful to patients. Of these increases, 30.3% happened suddenly, 42.4% gradually, and for 27.3% criteria were inconclusive. Quantitative and qualitative criteria showed a very high agreement (Cohen's κ = 0.85) regarding if a participant experienced a recurrence of depression, but a moderate agreement (Cohen's κ = 0.49) regarding how that change occurred. At the micro-level, 41.1% of participants experienced only sudden increases in depressed mood, 12.5% only gradual, 30.4% experienced both types of increase, and 16.1% neither. CONCLUSION: Meaningful change is common in patients discontinuing antidepressants, and there is substantial heterogeneity in how and when these changes occur. Depressive symptom change at the macro-level is not the same as depressive symptom change at the micro-level.

Automaticity and depression: Daily mood-reactive rumination in people with and without depression history.

Depressive rumination has been conceptualized as a mental habit that is initiated automatically without conscious awareness, intent, or control in response to negative mood. However, it is unknown whether depression vulnerability is characterized by elevated levels of mood-reactive rumination at the level of short-term dynamics. Using mobile ecological momentary assessment, formerly depressed individuals with a recurrent history of depression (n = 94) and nonclinical controls (n = 55) recorded in-the-moment affect and rumination 10 times daily over 6 days, after completing baseline measures of trait ruminative brooding, early life stress, and habitual characteristics of negative thinking (e.g., automaticity, lack of conscious awareness, intent, and control). Momentary fluctuations in negative affect were prospectively associated with greater rumination at the next sampling occasion in formerly depressed participants whereas this pattern of mood-reactive rumination was not observed in nonclinical controls. In formerly depressed participants, habitual characteristics of negative thinking was associated with greater mood-reactivity of rumination, particularly among those with a history of early life stress. Mood-reactive rumination was not, however, associated with depression course nor with the frequency of trait ruminative brooding. Rumination may be triggered in response to negative affect with a high degree of automaticity, making it difficult to control. Greater mood-reactivity of rumination might be associated with increased depression risk, independent of the depressive course and may be exacerbated by early life stress. Future studies may need to go beyond frequency and focus on the role of mood-reactivity and automaticity of ruminative thinking in depression vulnerability. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Psychopathological networks: Theory, methods and practice.

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Using structural equation modeling to study traits and states in intensive longitudinal data.

Traditionally, researchers have used time series and multilevel models to analyze intensive longitudinal data. However, these models do not directly address traits and states which conceptualize the stability and variability implicit in longitudinal research, and they do not explicitly take into account measurement error. An alternative to overcome these drawbacks is to consider structural equation models (state-trait SEMs) for longitudinal data that represent traits and states as latent variables. Most of these models are encompassed in the latent state-trait (LST) theory. These state-trait SEMs can be problematic when the number of measurement occasions increases. As they require the data to be in wide format, these models quickly become overparameterized and lead to nonconvergence issues. For these reasons, multilevel versions of state-trait SEMs have been proposed, which require the data in long format. To study how suitable state-trait SEMs are for intensive longitudinal data, we carried out a simulation study. We compared the traditional single level to the multilevel version of three state-trait SEMs. The selected models were the multistate-singletrait (MSST) model, the common and unique trait-state (CUTS) model, and the trait-state-occasion (TSO) model. Furthermore, we also included an empirical application. Our results indicated that the TSO model performed best in both the simulated and the empirical data. To conclude, we highlight the usefulness of state-trait SEMs to study the psychometric properties of the questionnaires used in intensive longitudinal data. Yet, these models still have multiple limitations, some of which might be overcome by extending them to more general frameworks. (PsycInfo Database Record (c) 2022 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.

Person-specific networks in psychopathology: Past, present, and future.

In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

Do daily mood fluctuations activate ruminative thoughts as a mental habit? Results from an ecological momentary assessment study.

It has been suggested that mental habits may underpin a heightened disposition to engage in rumination in response to negative mood. The aim of the current study was to assess the role of habit in the dynamic interplay between affect and ruminative thinking in the flow of daily life experiences. Using mobile ecological momentary assessment, 97 participants recorded affect and rumination ten times daily over six days, after completing measures of trait ruminative brooding and habitual characteristics of negative thinking (e.g. automaticity, lack of conscious awareness, intent and control). Momentary fluctuations in negative (increased) and positive (decreased) affect was prospectively associated with greater rumination-levels at the next sampling occasion. The degree to which affect triggered a subsequent ruminative response was moderated by habitual characteristics of negative thinking in a theoretically consistent way. Stronger temporal pairing of negative affect and rumination was also associated with greater emotional inertia but less carry-over of rumination from one moment to the next. Depression vulnerability may be in the form of rumination being habitually triggered in response to momentary fluctuations in affect, with deleterious effect on mood. The findings may have clinical implications, as targeting the habitual nature of rumination might help reduce depression vulnerability.

Early warning signals and critical transitions in psychopathology: challenges and recommendations.

Empirical evidence is mounting that monitoring momentary experiences for the presence of early warning signals (EWS) may allow for personalized predictions of meaningful symptom shifts in psychopathology. Studies aiming to detect EWS require intensive longitudinal measurement designs that center on individuals undergoing change. We recommend that researchers (1) define criteria for relevant symptom shifts a priori to allow specific hypothesis testing, (2) balance the observation period length and high-frequency measurements with participant burden by testing ambitious designs with pilot studies, and (3) choose variables that are meaningful to their patient group and facilitate replication by others. Thoroughly considered designs are necessary to assess the promise of EWS as a clinical tool to detect, prevent, or encourage impending symptom changes in psychopathology.