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The science of well-being lives at the heart of positive psychology. While the field has been around for 20 years, positive psychologists still face the challenge of finding optimal measurements of well-being. The PERMA framework (Seligman, 2011; 2018) can provide for a measurement model, but its added value in terms of being distinct from the already established measurement scale of Subjective Well-being (SWB; Deiner et al., 1999) has been under debate (Goodman et al., 2018). However, certain characteristics of PERMA make it unsuited for traditional factor analysis. In this paper we argue that a network approach is more suitable to understand the interrelational nature among the components of PERMA. We begin our investigation of PERMA by replicating Goodman and colleagues (2018) work. We then fit PERMA and two additional components to a network model. We then show how the components PERMA and SWB are distinct using exploratory graphical analysis. Our results show that while PERMA and SWB measure the same “type” of well-being, PERMA provides a measure that helps us understand the relationships that make up well-being that we refer to as the web of well-being.

The experience of love plays an integral role in human development as adolescents transition into adulthood. However, little is known about whether early adults have a shared understanding about indicators of love in daily life in this transitional phase. Using Cultural Consensus Theory informed by developmental theory, this study examined whether college-attending early adults in the United States reach a consensus on what makes people feel loved. One hundred sixty-six college-attending early adults ages 18 to 22 responded to 60 items on everyday scenarios and decided whether they thought most people would think each scenario was loving or not. Bayesian cognitive psychometric analysis revealed that college-attending respondents converged on a shared belief on love that included a wide range of everyday scenarios. Moreover, we found those higher conscientiousness and extraversion scores were more knowledgeable about the consensus on felt love. We expand on the developmental implications of these findings.

IntroductionEmotional eating is a mental health concern, common in adolescents, that develops as a result of their tendency to use high-energy food to regulate their fluctuating emotions. Due to their highly fluctuating emotional life, adolescents tend to have unique within-person profiles of emotional experiences that change across moments and days, often lost in global assessments of emotions. Hence, it is imperative to examine individual differences in dynamics of emotions, as experienced in daily life, in relation to emotional eating in adolescents.MethodsIn an Ecological Momentary Assessment study, we examined individual differences in three within-person dynamic characteristics (baseline levels, intra-individual variability, and emodiversity) of emotions in 158 dominantly Hispanic adolescents in the United States, aged 14-17 years old, predicting trait-level emotional eating.ResultsResults indicated that higher negative emodiversity, baselines, and variability in stress were predictive of emotional eating in adolescents. When all considered together, negative emodiversity (i.e., variety of the types of negative emotions experienced in one’s daily life) remained the only significant predictor of emotional eating.ConclusionsThis study affirms the importance of diversity in emotional experiences in relation to emotional eating, particularly in daily contexts of adolescents’ lives. Additionally, the study emphasizes the importance of distinguishing between diversity (i.e., variety in types) in positive versus negative emotional experiences with regard to emotional eating. By taking into account the ecological validity of adolescents’ daily lives and individual differences in dynamical changes in emotions, we are taking a step forward by shedding light on how the dynamics of negative emotions—in terms of within-person baselines, variability, and diversity— might be related to general levels of emotional eating in adolescents.

While the scientific study of well-being has a rich history, there are still challenges to establishing a clear definition and method of measurement due to the complexity of the construct and its cultural dependencies. In order to gain insight into how this field has progressed over time and what remains unaddressed, a comprehensive overview of the scientific pursuit of well-being and its future trajectories is needed. Previous reviews on well-being tend to have limited scope or contain subjective inferences about the state of research on well-being, resulting in fragmented insights and a lack of a comprehensive view of the research on well-being. To address this limitation, we used a database of over 30,000 primary documents downloaded from Web of Science, and leverage three bibliometric methods—historiography, document co-citation analysis, and bibliographic coupling—to map the intellectual structure of the entire field of well-being science and provide a more comprehensive view of the research. The findings shed light on the (1) evolution of well-being science over time, (2) the underlying structure of the intellectual field and its current state, and (3) the future trajectory of the field and emerging topics. Based on our findings, we encourage well-being scholars to advance the field by embracing diverse populations and perspectives, transcend beyond reductionist views of well-being, and push towards a stronger science with advanced and rigorous methodologies.

The COVID-19 pandemic disrupted emerging adult first-year college students’ daily lives and well-being. Dialectical Behavior Therapy (DBT) recognizes that effective and adaptive emotion regulation skills can be important for this vulnerable population’s well-being, who are going through a transitioning phase of development in challenging times. An ecological momentary assessment study collected 1,796 data points from 76 first-year college students' daily usage of emotion regulation skills and momentary experiences of well-being (PERMA; Positive emotions, Engagement, Relationship, Meaning, Accomplishment) during COVID-19 in Spring 2020. Participants were 18 to 20 years of age, 71% female, 24% male, 4% non-binary, 1% preferred not to answer, 46% White, 36% Asian or Pacific Islander, 14% Hispanic or Latinx, 13% Black or African American, 13% mixed race, 1% Native American or Eskimo Aleut, and 4% did not specify their race/ethnicity. Results from multilevel models on intensive longitudinal data revealed that specific emotion regulation skills (accumulating positives, building mastery, coping ahead) consistently predict mPERMA even when controlling for dispositional well-being. Moreover, on days of sufficient sleep hours reported, students reported higher well-being levels; on days with more interaction with other people, students were more likely to engage in emotion regulation skill behaviors; and on weekends, students were less likely to engage in emotion regulation behaviors (but not avoid substances like alcohol). Findings add to the literature on momentary well-being and emotion regulation for the population of early adult first-year college students and the COVID-19 context.

Early adults face an important developmental transition that can be accompanied by increased risk for mental health issues and vulnerability to stress and adversity. Increased levels of psychological well-being (PWB) have been found to buffer this risk. Research shows that skills for improving PWB may be learned through PWB interventions; however, the dynamical mechanisms underlying this learning process are not well understood. We conducted an 8-week Randomized Control Trial (N=160; aged 18-22 years; middle- to high-SES; 77% White, 12% Asian, 6% black, 4% Latino, and 1% did not disclose their race), implemented in a mobile Health (mHealth) framework. We had three groups: an active control group (N=55); an intervention group (N=51) with positive practice intervention (PPI); and a second intervention group (N=54) with positive practice and meditation intervention. Assessments and interventions were all delivered via the participant’s mobile device. The mHealth implementation allowed as to assess the participants’ PWB levels several times a day, providing rich data for understanding the dynamics of change. We used a process-oriented approach in combination with a Bayesian model (dubbed as GoHiAR) to analyze changes in PWB in terms of dynamical characteristics of the learning process. Findings suggested that the mobile assessment tool itself may have longitudinally improved college student PWB, as evidenced by instances of directional changes in dynamic characteristics (increased mean levels, decreased fluctuation, and increased regulation) of PWB measures. Moreover, although the mHealth interventions did not add benefit over mobile assessment at the group level overall, they improved PWB for students who had low baseline negative affect levels.

This study introduces the concept of felt love as the monadic experience of love, a parallel of dyadic love, and presents a study examining the relationships between daily felt love and sleep quality across 28 days. Before beginning the daily protocol, participants answered 60 questions assessing common situations that may make people feel loved. These questions were used to establish a consensus on the meaning of felt love for the specific participants under study. During the daily life study, participants (N = 52) provided self-reports via smartphone surveys for 28 days. Each morning upon waking, participants assessed their subjective sleep quality, and each evening positive affect was assessed with a set of 5 questions. Participants also rated how much they felt loved at 6 semirandom times throughout the day. Felt love measures were aggregated to obtain daily means and individual means across the study. Variability in felt love was quantified by calculating within-day and between-day standard deviations. Multilevel modeling was used to account for repeated measurements for each participant across the study, and the final model includes age, sex, positive affect, and felt love as predictors of sleep quality. Participants who reported higher positive affect and higher within-day felt love variability reported better overall sleep quality. However, overall mean levels of felt love did not significantly predict sleep quality, nor were there effects at the daily level. These results suggest that experiencing a range of felt love intensities within an average day is associated with better sleep quality.

In intensive longitudinal designs individuals are measured with high frequency over time. Sometimes there are multiple bursts of measurement, yielding multi-phase longitudinal data, which allow for evaluation of behavioral change at multiple timescales. From a systems theory perspective, a process that exhibits change occurring simultaneously at multiple time scales is non-stationary. Although recent methodological developments, such as dynamic systems models with time-varying parameters, have the capacity of handling non-stationarity, a more comprehensive framework may be needed for better analyzing multi-subject and multi-phase (interrupted) time series data. In this study, we propose a Bayesian growth of hierarchical autoregression (GoHiAR) model, which combines autoregressive (AR) and growth curve models (GCM) to simultaneously evaluate developmental changes in multifaceted dynamic characteristics (e.g., baseline, variability, and regulation) of psychological processes as well as individual differences in these changes. We applied the proposed model to an ecological momentary assessment (EMA) study that had an mHealth intervention phase to investigate changes of dynamic characteristics of psychological well-being process, specifically in meaning of life. Findings suggested that participants generally experienced decreased fluctuation and increased regulation of meaning of life over the course of the intervention, but the mHealth intervention did not result in credible added benefit.

We introduce a theoretical framework for conceptualizing psychological well-being as a process that unfolds over short and longer time-scales. We argue that this framework can be especially useful for studying the change mechanisms in psychological well-being within the context of mobile Health (mHealth) interventions. Four lines of process-oriented research are considered within this framework to inform the scientific exploration of psychological well-being in the context of mHealth interventions. First, we explore psychological well-being functioning via person-specific characteristics. Second, we discuss key characteristics of change as a result of mHealth interventions. Third, we review evidence that interventions conduce towards psychological well-being through an underlying process of skill development through practice. Fourth, we discuss intervention response heterogeneity within this framework. The approach we outline is intended to articulate currently unanswered, process-oriented research questions about psychological well-being interventions, how they work, and a methodological path forward for exploring them.

It is widely acknowledged that social relationships unfold across multiple time-scales. For example, social interactions that take place over moments, hours, or days also shape relationship change and outcomes over months, years, and even decades. These processes likely unfold in the reverse direction as well: experiences over longer-term timeframes may shape how people interact on a moment-to-moment basis. The increasing availability of intensive longitudinal data and measurement burst designs, as well as creativity in the contexts and modes of data collection--including social media, text messages, and event-contingent sampling--have exploded the possibilities for advancing knowledge on social relationships across multiple time-scales. The aim of this issue is to forward methodological innovations, empirical findings, and future directions in relationship science through the study of social and personal relationships across multiple time-scales.

Purpose of Review Effective treatment of anxiety-related disorders is crucial, considering the prevalence of such disorders and their association with poor psychosocial functioning. To evaluate the most recent evidence on the efficacy of cognitive behavioral therapy (CBT) for anxiety-related disorders in adults, we conducted a meta-analysis of randomized placebocontrolled trials published since 2017. Recent Findings Ten studies with a total of 1250 participants met the inclusion criteria. Seven of these studies examined PTSD. The findings demonstrated small placebo-controlled effects of CBT on target disorder symptoms (Hedges’ g = 0.24, p < 0.05) and depression (Hedges’ g = 0.15, p = n.s). When examining only PTSD studies, effects were reduced (Hedges’ g = 0.14, p < 0.05). Heterogeneity in most analyses was very low, and no publication bias was found. Summary Effect sizes from placebo-controlled trials from the past 5 years appear to be smaller than those in prior metaanalyses. The findings are largely driven by research on PTSD, with few placebo-controlled trials of other anxiety-related disorders published since 2017., Alexander von Humboldt Foundation, United States Department of Health & Human Services National Institutes of Health (NIH) - USA R01AT007257 United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) R01MH099021 U01MH108168, James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative, Projekt DEAL

Despite the growing deployment of network representation throughout psychological sciences, the question of whether and how networks can systematically describe the effects of psychological interventions remains elusive. Towards this end, we capitalize on recent breakthrough in network control theory, the engineering study of networked interventions, to investigate a representative psychological attitude modification experiment. This study examined 30 healthy participants who answered 11 questions about their attitude toward eating meat. They then received 11 arguments to challenge their attitude on the questions, after which they were asked again the same set of questions. Using this data, we constructed networks that quantify the connections between the responses and tested: 1) if the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network topology as described by control theory, 2) if the size of change in responses relates to whole-network topology that quantifies the “ease” of change as described by control theory, and 3) if responses after intervention could be predicted based on formal results from control theory. We found that 1) the interventions that had higher regional topological relevance (the so-called controllability scores) had stronger effect (r > 0.5), the intervention sensitivities were systematically lower for the interventions that were “easier to control” (r = -0.49), and that the model offered substantial prediction accuracy (r = 0.36). Finally, we compared results based on subjective as well as data-driven models and found that, across all tests, the results from the data-driven approach explained a larger size of effect. Taken together, our results suggest that psychological interventions can be studied using network control theory and that data-driven models provide a reliable medium.

The transitional years of early adulthood engenders both opportunities and risks for well-being. In this paper, we propose that the conceptualization and measurement of early adult’s well-being during early adulthood can be improved through the use of short, daily momentary assessments of well-being, and through an examination of the structure of well-being within (and not just across) time. We developed items to assess daily well-being based on the PERMA model of well-being (Seligman, 2011). Using Ecological Momentary Assessments, we assessed the five PERMA elements in daily life and their network properties. Consistent with Seligman (2011), network analysis showed items (nodes) clustered around theorized elements, and also formed a unitary network of well-being. Furthermore, we found that positive Relationships and Positive emotion items were the most central aspects of early adults’ daily well-being. Results have implications for the putative targets for wellbeing-oriented intervention and prevention programs during this period of life.

Cultural consensus theory is a statistical framework (CCT) for the study of individual differences in the knowledge of culturally shared opinions. In this article, we demonstrate how a CCT analysis can be used to study individual differences and cultural consensus on what makes people feel loved, or more generally any social behaviors that are governed by cognitive schemata. To highlight the advantages of the method, we describe a study in which people reported on their everyday experiences of feeling loved. Our unique approach to understanding this topic is to focus on people’s cognitive evaluations on what feeling loved (both romantically and nonromantically) entails by exploring the shared agreement regarding when one is most likely to feel loved and the individual differences that influence knowledge of these shared agreements. Our results reveal that people converge on a consensus about indicators of expressed love and that these scenarios are both romantic and nonromantic. Moreover, people show individual differences in (1) the amount of knowledge they have about this consensus and (2) their guessing biases in responding to items on love scenarios, depending on personality and demographics—all conclusions made possible by the CCT method.

The importance of studying specific and expressed emotions after a stressful life event is well known, yet few studies have moved beyond assessing self-reported emotional responses to a romantic breakup. This study examined associations between computer-recognized facial expressions and self-reported breakup-related distress among recently separated college-aged young adults (N = 135; 37 men) on four visits across 9 weeks. Participants' facial expressions were coded using the Computer Expression Recognition Toolbox while participants spoke about their breakups. Of the seven expressed emotions studied, only Contempt showed a unique association with breakup-related distress over time. At baseline, greater Contempt was associated with less breakup-related distress; however, over time, greater Contempt was associated with greater breakup-related distress.