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.

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.

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.

A template and tutorial for preregistering studies using passive smartphone measures.

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures.

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.

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.

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.

ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data.

PURPOSE: The experience sampling method (ESM) is used for intensive longitudinal time-series data collection during normal daily life. ESM data give information on momentary affect, activities and (social) context of, for example, patients suffering from mental disorders, and allows for person-specific feedback reports. However, current personalized feedback reports only display a selection of measured variables, and typically involve only summary statistics, thus not reflecting the dynamic fluctuations in affect and its influencing factors. To address this shortcoming, we developed a tool for dynamically visualizing ESM data. METHODS: We introduce a new framework, ESMvis, for giving descriptive feedback, focusing on direct visualization of the dynamic nature of raw data. In this ESM feedback approach, raw ESM data are visualized using R software. We applied ESMvis to data collected for over 52 weeks on a patient diagnosed with an obsessive-compulsive disorder with comorbid depression. RESULTS: We provided personalized feedback, in which both the overall trajectory and specific time moments were captured in a movie format. Two relapses during the study period could be visually determined, and subsequently confirmed by the therapist. The therapist and patient evaluated ESMvis as an insightful add-on tool to care-as-usual. CONCLUSION: ESMvis is a showcase on providing personalized feedback by dynamic visualization of ESM time-series data. Our tool is freely available and adjustable, making it widely applicable. In addition to potential applications in clinical practice, ESMvis can work as an exploratory tool that can lead to new hypotheses and inform more complex statistical techniques.

A Tutorial on Estimating Time-Varying Vector Autoregressive Models.

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements.

Using the time-varying autoregressive model to study dynamic changes in situation perceptions and emotional reactions.

OBJECTIVE: Assuming personality to be a system of intra-individual processes emerging over time in interaction with the environment, we propose an idiographic approach to investigate potential changes of intra-individual dynamics in the perception of situations and emotions of individuals varying in personality traits. We compared the semiparametric time-varying autoregressive model (TV-AR) that takes into account the non-stationarity of psychological processes at the individual level, with the standard AR model. METHOD: We conducted analyses of individual time series to assess intra-individual changes in mean levels and inertia on data from two adolescents who completed measures of personality and indicated their situation perceptions and emotions five times a day for 19 days. RESULTS: For the less honest, emotional, extraverted, and more agreeable adolescent, the TV-AR model detected reliable changes in the intra-individual dynamics of situation perceptions and emotions whereas, for the other individual, the standard AR model was more preferred, given the lack of changes in the intra-individual dynamics. CONCLUSIONS: Psychological processes dynamics in situation perception and emotions may vary from person to person depending on their personality. This work constitutes a first step in demonstrating that an idiographic approach has advantages in identifying changes in individuals' perceptions and reactions to situations.

Networks, intentionality and multiple realizability: Not enough to block reductionism.

Borsboom et al. propose that the network approach blocks reductionism in psychopathology. We argue that the two main arguments, intentionality and multiple realizability of mental disorders, are not sufficient to establish that mental disorders are not brain disorders, and that the specific role of networks in these arguments is unclear.

Cross-sectional networks of depressive symptoms before and after antidepressant medication treatment.

PURPOSE: Recent reviews have questioned the efficacy of selective serotonin reuptake inhibitors (SSRIs) above placebo response, and their working mechanisms remain unclear. New approaches to understanding the effects of SSRIs are necessary to enhance their efficacy. The aim of this study was to explore the possibilities of using cross-sectional network analysis to increase our understanding of symptom connectivity before and after SSRI treatment. METHODS: In two randomized controlled trials (total N = 178), we estimated Gaussian graphical models among 20 symptoms of the Beck Depression Inventory-II before and after 8 weeks of treatment with the SSRI paroxetine. Networks were compared on connectivity, community structure, predictability (proportion explained variance), and strength centrality (i.e., connectedness to other symptoms in the network). RESULTS: Symptom severity for all individual BDI-II symptoms significantly decreased over 8 weeks of SSRI treatment, whereas interconnectivity and predictability of the symptoms significantly increased. At baseline, three communities were detected; five communities were detected at week 8. CONCLUSIONS: Findings suggest the effects of SSRIs can be studied using the network approach. The increased connectivity, predictability, and communities at week 8 may be explained by the decrease in depressive symptoms rather than specific effects of SSRIs. Future studies with larger samples and placebo controls are needed to offer insight into the effects of SSRIs. TRIAL REGISTRATION: The trials described in this manuscript were funded by the NIMH. Pennsylvania/Vanderbilt study: 5 R10 MH55877 ( https://projectreporter.nih.gov/project_info_description.cfm?aid=6186633&icde=28344168&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&MMOpt= ). Washington study: R01 MH55502 ( https://projectreporter.nih.gov/project_info_description.cfm?aid=2034618&icde=28344217&ddparam=&ddvalue=&ddsub=&cr=5&csb=default&cs=ASC ).

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

Putting the "experience" back in experience sampling: A phenomenological approach.

This article discusses the concept of "experience" in experience sampling. A central challenge of clinical science is understanding psychopathological constructs and their manifestations. In conventional definitions and measures of psychopathology, subjective experience of mental disorder is often lost. The authors argue for an integration of phenomenology-or prioritization of subjectivity-in psychopathological construct definition and measurement, particularly through experience sampling methods (ESMs). ESMs capture idiographic, contextual, and longitudinal elements of lived experience that can expand our current conceptualizations and classifications of psychopathology. The authors propose three novel applications and extensions: (a) leveraging ESM for subjective construct definition (i.e., phenomena detection), (b) mixed-methods approaches, like cognitive interviewing, to improve the validity of ESM measures and (c) incorporation of novel ESM approaches (e.g., audiovisual data capturing) to expand understanding of subjective, daily experience of psychopathology. Merging phenomenological tradition with ESM serves to expand our understanding of psychopathology and bring "experience" back into experience sampling. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

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).

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.

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.