Adolescent Emotion Network Dynamics in Daily Life and Implications for Depression

Subgrouping with Chain Graphical VAR Models

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

A socioemotional network perspective on momentary experiences of family conflict in young adults

Family conflict is an established predictor of psychopathology in youth. Traditional approaches focus on between-family differences in conflict. Daily fluctuations in conflict within families might also impact psychopathology, but more research is needed to understand how and why. Using 21 days of daily diary data and 6-times a day experience-sampling data (N = 77 participants; mean age = 21.18, SD = 1.75; 63 women, 14 men), we captured day-to-day and within-day fluctuations in family conflict, anger, anxiety, and sadness. Using multilevel models, we find that days of higher-than-usual anger are also days of higher-than-usual family conflict. Examining associations between family conflict and emotions within days, we find that moments of higher-than-usual anger predict higher-than-usual family conflict later in the day. We observe substantial between-family differences in these patterns with implications for psychopathology; youth showing the substantial interplay between family conflict and emotions across time had a more perseverative family conflict and greater trait anxiety. Overall, findings indicate the importance of increases in youth anger for experiences of family conflict during young adulthood and demonstrate how intensive repeated measures coupled with network analytic approaches can capture long-theorized notions of reciprocal processes in daily family life.

Emotion Regulation Predicts Depressive Symptoms in Adolescents: A Prospective Study

Emotion regulation as a proximal factor has been linked with depressive symptoms. However, studies have mainly focused on a limited number of strategies and have mostly been cross-sectional in design. This is particularly evident when examining the protective effects of adaptive strategies. This study aimed to investigate the prospective relationship between putatively adaptive and maladaptive emotion regulation strategies and depressive symptoms among adolescents. Additionally, a person-oriented approach was applied to identify latent classes of adolescents based on their depressive symptoms and compared these classes in terms of their adaptive and maladaptive strategies. Two waves of data from a prospective study, which included 1371 youth (mean age: 15.66 years; SD = 0.49 years; 55.1% girls), were analysed. The two points of data collection were spaced approximately half a year apart. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale, and putatively adaptive and maladaptive emotion regulation strategies were assessed with the Cognitive Emotion Regulation Questionnaire. Seven strategies (acceptance, positive refocusing, positive reappraisal, putting into perspective, self-blame, rumination, and catastrophizing) were categorised into adaptive and maladaptive factors using exploratory structural equation modeling. After controlling for gender, age, and depressive symptoms at Time 1, both maladaptive and adaptive emotion regulation strategies at Time 1 predicted depressive symptoms at Time 2. Three subgroups emerged based on the intensity of depressive symptoms across the waves: the stable low, stable moderate, and stable high depressive symptom groups. The use of maladaptive emotion regulation strategies (such as rumination, self-blame, and catastrophizing) at Time 1 was more pronounced in the stable moderate and high symptom groups compared to the stable low depressive symptom group. The comparable prospective associations between putatively adaptive and maladaptive strategies with symptoms suggest the need to identify factors that may mitigate the negative impact of maladaptive emotion regulation and/or promote adaptive emotion regulation to buffer the effects of everyday stressors.

Network analysis of discrete emotional states measured via ecological momentary assessment in schizophrenia

Prior studies demonstrate that schizophrenia (SZ) is associated with abnormalities in positive and negative emotional experience that predict clinical presentation. However, it is unclear whether specific discrete emotions within the broader positive/negative categories are driving those symptom associations. Further, it is also unclear whether specific emotions contribute to symptoms in isolation or via networks of emotional states that dynamically interact across time. The current study used network analysis to evaluate temporally dynamic interactions among discrete emotional states experienced in the real world as assessed via Ecological Momentary Assessment (EMA). Participants included 46 outpatients with chronic SZ and 52 demographically matched healthy controls (CN) who completed 6 days of EMA that captured reports of emotional experience and symptoms derived from monetary surveys and geolocation based symptom markers of mobility and home location. Results indicated that less dense emotion networks were associated with greater severity of negative symptoms, whereas more dense emotion networks were associated with more severe positive symptoms and mania. Additionally, SZ evidenced greater centrality for shame, which was associated with greater severity of positive symptoms. These findings suggest that positive and negative symptoms are associated with distinct profiles of temporally dynamic and interactive emotion networks in SZ. Findings have implications for adapting psychosocial therapies to target specific discrete emotional states in the treatment of positive versus negative symptoms.

Elevated emotion network connectivity is associated with fluctuations in depression

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

The added value of daily diary data in 1- and 3-year prediction of psychopathology and psychotic experiences in individuals at risk for psychosis

This study aimed to assess whether adding information on psychological experiences derived from a daily diary to baseline cross-sectional data could improve short- (1-year) and long-term (3-years) prediction of psychopathology and positive psychotic experiences (PEs). We used 90-day daily diary data from 96 individuals in early subclinical risk stages for psychosis. Stepwise linear regression models were built for psychopathology and PEs at 1- and 3-years follow-up, adding: (1) baseline questionnaires, (2) the mean and variance of daily psychological experiences, and (3) individual symptom network density. We assessed whether similar results could be achieved with a subset of the data (7-14- and 30-days). The mean and variance of the diary improved model prediction of short- and long-term psychopathology and PEs, compared to prediction based on baseline questionnaires solely. Similar results were achieved with 7-14- and 30-day subsets. Symptom network density did not improve model prediction except for short-term prediction of PEs. Simple metrics, i.e., the mean and variance from 7 to 14 days of daily psychological experiences assessments, can improve short- and long-term prediction of both psychopathology and PEs in individuals in early subclinical stages for psychosis. Diary data could be a valuable addition to clinical risk prediction models for psychopathology development.

Longitudinal relationships in the psychopathology of depressive symptoms in middle-aged and older adults in China

OBJECTIVES

To develop symptom networks and examine the longitudinal relationships of depressive symptoms among middle-aged and older adults in China.

METHOD

This study used three-wave data from the China Health and Retirement Longitudinal Study (2013 (T1), 2015 (T2), and 2018 (T3)). Depressive symptoms were measured by the 10-item Center for Epidemiologic Studies Depression Scale (CES-D). A multilevel vector autoregression model (VAR) was used to identify ten depressive symptoms dynamically interacting with each other over time.

RESULTS

A total of 3,558 participants were included in the final analysis. The strongest direct effects were 'D10: felt fearful' -> 'D6: felt everything I did was an effort' (β = 0.14). 'D10: felt fearful' reported the largest value of out-predictability (r = 0.064) and out-strength (r = 0.635). 'D3: felt depressed' reported the largest value of in-predictability (r = 0.077) and in-strength (r = 0.545). Substantial heterogeneity in the network may stem from an individual's sex and place of residence.

CONCLUSIONS

'Felt fearful' was the strongest predictor compared to the other nine depressive symptoms based on node centrality. Our study suggests that, after understanding the causes of fear, strategies to reduce fear should be incorporated into multimodal interventions for middle-aged and older adults with depressive symptoms.

Associations between valenced news and affect in daily life: Experimental and ecological momentary assessment approaches

In 203 (mean age = 38.04 years, SD=12.05) participants, we tested the association between valenced news and affect using a 14-day, smartphone-based ecological momentary assessment protocol consisting of two components: 1) a once-per-day experimental protocol in which participants were exposed to good news and bad news stories and 2) a four-times-per-day protocol capturing ecological fluctuations in news consumption. Across both protocols, we replicate findings that consumption of positively valenced news is associated with increased positive affect and decreased negative affect while consumption of negatively valenced news is associated with increased negative affect and decreased positive affect. By integrating the ecological momentary assessment data with network science methodologies, news selection and news effects were modeled simultaneously, uncovering selection processes whereby current positive affect, but not negative affect, predicted future valenced news consumption. Altogether, findings indicate that everyday news consumption influences positive and negative affect and may serve mood management functions for positive but not negative affect.

Within-Person Temporal Associations Among Self-Reported Physical Activity, Sleep, and Well-Being in College Students

OBJECTIVE

A holistic understanding of the naturalistic dynamics among physical activity, sleep, emotions, and purpose in life as part of a system reflecting wellness is key to promoting well-being. The main aim of this study was to examine the day-to-day dynamics within this wellness system.

METHODS

Using self-reported emotions (happiness, sadness, anger, anxiousness) and physical activity periods collected twice per day, and daily reports of sleep and purpose in life via smartphone experience sampling, more than 28 days as college students ( n = 226 young adults; mean [standard deviation] = 20.2 [1.7] years) went about their daily lives, we examined day-to-day temporal and contemporaneous dynamics using multilevel vector autoregressive models that consider the network of wellness together.

RESULTS

Network analyses revealed that higher physical activity on a given day predicted an increase of happiness the next day. Higher sleep quality on a given night predicted a decrease in negative emotions the next day, and higher purpose in life predicted decreased negative emotions up to 2 days later. Nodes with the highest centrality were sadness, anxiety, and happiness in the temporal network and purpose in life, anxiety, and anger in the contemporaneous network.

CONCLUSIONS

Although the effects of sleep and physical activity on emotions and purpose in life may be shorter term, a sense of purpose in life is a critical component of wellness that can have slightly longer effects, bleeding into the next few days. High-arousal emotions and purpose in life are central to motivating people into action, which can lead to behavior change.

High- and Low-Arousal Daily Affect Dynamics Vary Across the Adult Lifespan

OBJECTIVES

Age differences in affective experience across adulthood are widely documented. According to the circumplex model of affect consists of 2 aspects-valence (positive vs negative) and arousal (low activation vs high activation). Prior research on age differences has primarily focused on the valence aspect. However, little is known about age differences in daily affect of high and low arousal.

METHOD

The present study examined age differences in daily dynamics (i.e., mean levels, variability, and inertia) of negative affect (NA) and positive affect (PA) of high and low arousal in a sample of 492 adults aged 21-91. Participants completed daily affect ratings for 21 consecutive days.

RESULTS

Age was negatively and linearly related to mean levels of both high-arousal and low-arousal NA. Both high-arousal and low-arousal PA mean levels showed increases after middle age. Further, age was related to lower variability in both NA and PA regardless of arousal. Additionally, high-arousal NA inertia showed a linear decrease with age, whereas low-arousal PA inertia showed an inverted-U pattern with age. After controlling for mean levels of affect, the associations between age and affect variability remained significant, whereas the associations between age and affect inertia did not.

DISCUSSION

The affective profile of older age is characterized by lower mean levels of NA, higher mean levels of PA, lower affect variability, and less persistence in high-arousal NA and low-arousal PA in daily life. Our results contribute to a nuanced understanding of which affective processes improve with age and which do not.

Exploring High vs. Low Burnout amongst Public Sector Educators: COVID-19 Antecedents and Profiles

The COVID-19 pandemic has had a prolonged impact on many people working in different sectors. This paper focuses on the psychological stress consequences of professionals working in the educational sector in Andalucía (Spain). Using a sample of 340 educators, this empirical paper identifies the antecedents and profiles of those that ended up with burnout vs. those that were able to develop resilience. Results from OLS regressions show that regardless of the origins of stress, the principal determinant of burnout is clearly a lack of support and a perception of an inability to control a situation. Furthermore, results also show that working sources have a higher impact on the configuration of high burnout, while family sources harm those who are more resilient (low burnout).

Emotion network density in burnout

Background

Health care workers are often affected by burnout, resulting in reduced personal well-being and professional functioning. Although emotional exhaustion is considered a core component of burnout, little is known about the dynamics of emotions and their relation to burnout. We used network analysis to investigate the correlation between the density of a negative emotion network, a marker for emotional rigidity in person-specific networks, and burnout severity.

Methods

Using an ecological momentary assessment design, the intensity of negative emotions of forty-three health care workers and medical students was assessed five times per day (between 6 am and 8 pm) for 17 days. Burnout symptoms were assessed at the end of the study period with the Maslach Burnout Inventory. Multilevel vector autoregressive models were computed to calculate network density of subject-specific temporal networks. The one-sided correlation between network density and burnout severity was assessed. The study protocol and analytic plan were registered prior to the data collection.

Results

We found a medium-sized correlation between the negative emotion network density and burnout severity at the end of the study period r(45) = .32, 95% CI = .09–1.0, p = .014).

Conclusions

The strength of the temporal interplay of negative emotions is associated with burnout, highlighting the importance of emotions and emotional exhaustion in reaction to occupational-related distress in health care workers. Moreover, our findings align with previous investigations of emotion network density and impaired psychological functioning, demonstrating the utility of conceptualizing the dynamics of emotions as a network.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00670-y.

Emotion networks across self-reported depression levels during the COVID-19 pandemic

ABSTRACTDuring stressful circumstances, such as the COVID-19 pandemic, disturbances in emotional experiences can occur. These emotional disturbances, if not relieved or regulated, can be associated with feelings of depression. Currently, little is known about which emotional experiences (positive and negative) are associated with feelings of depression during COVID-19. This study aimed to estimate and compare mixed, positive and negative valence emotion networks during COVID-19 for low, moderate and high levels of self-reported depression. Across 26,034 participants, central emotional experiences included gratitude, sadness, fear, anxiety, compassion, and being moved for all self-reported depression levels; love for low levels of depression, and confusion for high levels of depression. The strongest edges included fear-anxiety, loneliness-boredom, anger-disgust, determination-hope, and compassion-being moved for all self-reported depression levels; calm-relief, and sadness-frustration for high levels of self-reported depression; and admiration-being moved for low and moderate self-reported depression levels. Network comparison tests showed mixed, positive and negative emotion networks significantly differed in structure across all self-reported depression levels. Network connectivity was also significantly stronger for low self-reported depression within positive and negative emotion networks. These networks provide key information on emotional experiences associated with depression during COVID-19.

Dynamic time warp analysis of individual symptom trajectories in depressed patients treated with electroconvulsive therapy

BACKGROUND

Although electroconvulsive therapy (ECT) effectively improves severity scores of depression, its effects on its individual symptoms has scarcely been studied. We aimed to study which depressive symptom trajectories dynamically cluster together in individuals as well as groups of patients during ECT using Dynamic Time Warp (DTW) analysis.

METHODS

We analysed the standardized weekly scores on the 25-item abbreviated version of the Comprehensive Psychopathological Rating Scale (CPRS) in depressed patients before and during their first six weeks of ECT treatment. DTW analysis was used to analyse the (dis)similarity of time series of items scores at the patient level (300 'DTW distances' per patient) as well as on the group level. Hierarchical cluster, network, and Distatis analyses yielded symptom dimensions.

RESULTS

We included 133 patients, 64.7% female, with an average age of 60.4 years (SD 15.1). Individual DTW distance matrices and networks revealed marked differences in hierarchical and network clusters among patients. Based on cluster analyses of the aggregated matrices, four symptom clusters emerged. In patients who reached remission, the average DTW distance between their symptoms was significantly smaller than non-remitters, reflecting denser symptom networks in remitters than non-remitters (p=0.04).

LIMITATIONS

The assessments were done only weekly during the first six weeks of ECT treatment. The use of individual items of the abbreviated CPRS may have led to measurement error as well as floor and ceiling effects.

CONCLUSION

DTW offers an efficient new approach to analyse symptom trajectories within individuals as well as groups of patients, aiding personalized medicine of psychopathology.

A Systematic Review and Lived-Experience Panel Analysis of Hopefulness in Youth Depression Treatment

Hopefulness is arguably of central importance to the recovery of youth with major or complex youth depression, yet it is unclear how hopefulness can best be enhanced in treatment. A narrative synthesis of published and grey literature was combined with new insights from a youth lived-experience panel (N = 15), focusing on to what extent and how specific psychological therapies and standard mental health care scaffold hopefulness as applied to depression among 14-25-year-olds. Thirty-one studies of variable quality were included in this review; thirteen were qualitative, thirteen quantitative, and five used mixed methods. Hopefulness is an important active ingredient of psychotherapies and standard mental health care in youth depression. Evidence suggests talking and activity therapies have moderate to large effects on hopefulness and that hopefulness can be enhanced in standard mental health care. However, varying intervention effects suggest a marked degree of uncertainty. Hopefulness is best scaffolded by a positive relational environment in which there is support for identifying and pursuing personally valued goals and engaging in meaningful activity. Animated (https://www.youtube.com/watch?v=o4690PdTGec) and graphical summaries (https://doi.org/10.13140/RG.2.2.27024.84487) are available.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40894-021-00167-0.

Emotion network density is a potential clinical marker for anxiety and depression: Comparison of ecological momentary assessment and daily diary

OBJECTIVES

Using two intensive longitudinal data sets with different timescales (90 minutes, daily), we examined emotion network density, a metric of emotional inflexibility, as a predictor of clinical-level anxiety and depression.

DESIGN

Mobile-based intensive longitudinal assessments.

METHODS

119 participants (61 anxious and depressed, 58 healthy controls) completed ecological momentary assessment (EMA) to rate a variety of negative (NE) and positive emotions (PE) 9 times per day for 8 days using a mobile phone application. 169 participants (97 anxious and depressed and 72 healthy controls) completed an online daily diary on their NE and PE for 50 days. Multilevel vector autoregressive models were run to compute NE and PE network densities in each data set.

RESULTS

In the EMA data set, both NE and PE network densities significantly predicted participants' diagnostic status above and beyond demographics and the mean and standard deviation of NE and PE. Greater NE network density and lower PE network density were associated with anxiety and depression diagnoses. In the daily diary data set, NE and PE network densities did not significantly predict the diagnostic status.

CONCLUSIONS

Greater inflexibility of NE and lower inflexibility of PE, indexed by emotion network density, are potential clinical markers of anxiety and depressive disorders when assessed at intra-daily levels as opposed to daily levels. Considering emotion network density, as well as the mean level and variability of emotions in daily life, may contribute to diagnostic prediction of anxiety and depressive disorders.

PRACTITIONER POINTS

Emotion network density, or the degree to which prior emotions predict and influence current emotions, indicates an inflexible or change-resistant emotion system. Emotional inflexibility or change resistance over a few hours, but not daily, may characterize anxiety and depressive disorders. Inflexible negative emotion systems are associated with anxiety and depressive disorders, whereas inflexible positive emotion systems may indicate psychological health. Considering emotional inflexibility within days may provide additional information beyond demographics and mean level and variability of emotions in daily life for detecting anxiety and depressive disorders. ​.

Network dynamics of momentary affect states and future course of psychopathology in adolescents

BACKGROUND

Recent theories argue that an interplay between (i.e., network of) experiences, thoughts and affect in daily life may underlie the development of psychopathology.

OBJECTIVE

To prospectively examine whether network dynamics of everyday affect states are associated with a future course of psychopathology in adolescents at an increased risk of mental disorders.

METHODS

159 adolescents from the East-Flanders Prospective Twin Study cohort participated in the study. At baseline, their momentary affect states were assessed using the Experience Sampling Method (ESM). The course of psychopathology was operationalized as the change in the Symptom Checklist-90 sum score after 1 year. Two groups were defined: one with a stable level (n = 81) and one with an increasing level (n = 78) of SCL-symptom severity. Group-level network dynamics of momentary positive and negative affect states were compared between groups.

RESULTS

The group with increasing symptoms showed a stronger connections between negative affect states and their higher influence on positive states, as well as higher proneness to form 'vicious cycles', compared to the stable group. Based on permutation tests, these differences were not statistically significant.

CONCLUSION

Although not statistically significant, some qualitative differences were observed between the networks of the two groups. More studies are needed to determine the value of momentary affect networks for predicting the course of psychopathology.

Modeling brain, symptom, and behavior in the winds of change

Neuropsychopharmacology addresses pressing questions in the study of three intertwined complex systems: the brain, human behavior, and symptoms of illness. The field seeks to understand the perturbations that impinge upon those systems, either driving greater health or illness. In the pursuit of this aim, investigators often perform analyses that make certain assumptions about the nature of the systems that are being perturbed. Those assumptions can be encoded in powerful computational models that serve to bridge the wide gulf between a descriptive analysis and a formal theory of a system's response. Here we review a set of three such models along a continuum of complexity, moving from a local treatment to a network treatment: one commonly applied form of the general linear model, impulse response models, and network control models. For each, we describe the model's basic form, review its use in the field, and provide a frank assessment of its relative strengths and weaknesses. The discussion naturally motivates future efforts to interlink data analysis, computational modeling, and formal theory. Our goal is to inspire practitioners to consider the assumptions implicit in their analytical approach, align those assumptions to the complexity of the systems under study, and take advantage of exciting recent advances in modeling the relations between perturbations and system function.

Temporal networks of tobacco withdrawal symptoms during smoking cessation treatment

A recently developed network perspective on tobacco withdrawal posits that withdrawal symptoms causally influence one another across time, rather than simply being indicators of a latent syndrome. Evidence supporting a network perspective would shift the focus of tobacco withdrawal research and intervention toward studying and treating individual withdrawal symptoms and intersymptom associations. Here we construct and examine temporal tobacco withdrawal networks that describe the interplay among withdrawal symptoms across time using experience-sampling data from 1,210 participants (58.35% female, 86.24% White) undergoing smoking cessation treatment. We also construct person-specific withdrawal networks and capture individual differences in the extent to which withdrawal symptom networks promote the spread of symptom activity through the network across time using impulse response analysis. Results indicate substantial moment-to-moment associations among withdrawal symptoms, substantial between-person differences in withdrawal network structure, and reductions in the interplay among withdrawal symptoms during combination smoking cessation treatment. Overall, findings suggest the utility of a network perspective and also highlight challenges associated with the network approach stemming from vast between-person differences in symptom networks. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Comparability of Emotion Dynamics Derived From Ecological Momentary Assessments, Daily Diaries, and the Day Reconstruction Method: Observational Study

Background

Interest in the measurement of the temporal dynamics of people’s emotional lives has risen substantially in psychological and medical research. Emotions fluctuate and change over time, and measuring the ebb and flow of people’s affective experiences promises enhanced insights into people’s health and functioning. Researchers have used a variety of intensive longitudinal assessment (ILA) methods to create measures of emotion dynamics, including ecological momentary assessments (EMAs), end-of-day (EOD) diaries, and the day reconstruction method (DRM). To date, it is unclear whether they can be used interchangeably or whether ostensibly similar emotion dynamics captured by the methods differ in meaningful ways.

Objective

This study aims to examine the extent to which different ILA methods yield comparable measures of intraindividual emotion dynamics.

Methods

Data from 90 participants aged 50 years or older were collected in a probability-based internet panel, the Understanding America Study, and analyzed. Participants provided positive and negative affect ratings using 3 ILA methods: (1) smartphone-based EMA, administered 6 times per day over 1 week, (2) web-based EOD diaries, administered daily over the same week, and (3) web-based DRM, administered once during that week. We calculated 11 measures of emotion dynamics (addressing mean levels, variability, instability, and inertia separately for positive and negative affect, as well as emotion network density, mixed emotions, and emotional dialecticism) from each ILA method. The analyses examined mean differences and correlations of scores addressing the same emotion dynamic across the ILA methods. We also compared the patterns of intercorrelations among the emotion dynamics and their relationships with health outcomes (general health, pain, and fatigue) across ILA methods.

Results

Emotion dynamics derived from EMAs and EOD diaries demonstrated moderate-to-high correspondence for measures of mean emotion levels (ρ≥0.95), variability (ρ≥0.68), instability (ρ≥0.51), mixed emotions (ρ=0.92), and emotional dialecticism (ρ=0.57), and low correspondence for measures of inertia (ρ≥0.17) and emotion network density (ρ=0.36). DRM-derived measures showed correlations with EMAs and EOD diaries that were high for mean emotion levels and mixed emotions (ρ≥0.74), moderate for variability (ρ=0.38-.054), and low to moderate for other measures (ρ=0.03-0.41). Intercorrelations among the emotion dynamics showed high convergence across EMAs and EOD diaries, and moderate convergence between the DRM and EMAs as well as EOD diaries. Emotion dynamics from all 3 ILA methods produced very similar patterns of relationships with health outcomes.

Conclusions

EMAs and EOD diaries provide corresponding information about individual differences in various emotion dynamics, whereas the DRM provides corresponding information about emotion levels and (to a lesser extent) variability, but not about more complex emotion dynamics. Our results caution researchers against viewing these ILA methods as universally interchangeable.

Within-person variability in curiosity during daily life and associations with well-being

OBJECTIVE

Curiosity promotes engagement in novel situations and the accruement of resources that promote well-being. An open question is the extent to which curiosity lability, the degree to which curiosity fluctuates over short timescales, impacts well-being.

METHOD

We use data from a 21-day daily diary as well as trait measures in 167 participants (mean age = 25.37 years, SD = 7.34) to test (a) the importance of curiosity lability for depression, flourishing, and life satisfaction, (b) day-to-day associations among curiosity and happiness, depressed mood, anxiety, and physical activity, and (c) the role of day's mood as a mediator between physical activity and curiosity.

RESULTS

We observe positive associations among curiosity lability and depression, as well as negative associations among curiosity lability and both life satisfaction and flourishing. Curiosity is higher on days of greater happiness and physical activity, and lower on days of greater depressed mood. We find evidence consistent with day's depressed mood and happiness being mediators between physical activity and curiosity.

CONCLUSIONS

Greater consistency in curiosity is associated with well-being. We identify several potential sources of augmentation and blunting of curiosity in daily life and provide support for purported mechanisms linking physical activity to curiosity via mood.

The Network Structure of Tobacco Withdrawal in a Community Sample of Smokers Treated With Nicotine Patch and Behavioral Counseling

INTRODUCTION

Network theories of psychopathology highlight that, rather than being indicators of a latent disorder, symptoms of disorders can causally interact with one another in a network. This study examined tobacco withdrawal from a network perspective.

METHODS

Participants (n = 525, 50.67% female) completed the Minnesota Tobacco Withdrawal Scale four times (2 weeks prior to a target quit day, on the target quit day, and 4 and 8 weeks after the target quit day) over the course of 8 weeks of treatment with nicotine patch and behavioral counseling within a randomized clinical trial testing long-term nicotine patch therapy in treatment-seeking smokers. The conditional dependence among seven withdrawal symptoms was estimated at each of the four measurement occasions. Influential symptoms of withdrawal were identified using centrality indices. Changes in network structure were examined using the Network Comparison Test.

RESULTS

Findings indicated many associations among the individual symptoms of withdrawal. The strongest associations that emerged were between sleep problems and restlessness, and associations among affective symptoms. Restlessness and affective symptoms emerged as the most central symptoms in the withdrawal networks. Minimal differences in the structure of the withdrawal networks emerged across time.

CONCLUSIONS

The cooccurrence of withdrawal symptoms may result from interactions among symptoms of withdrawal rather than simply reflecting passive indicators of a latent disorder. Findings encourage greater consideration of individual withdrawal symptoms and their potential interactions and may be used to generate hypotheses that may be tested in future intensive longitudinal studies.

IMPLICATIONS

This study provides a novel, network perspective on tobacco withdrawal. Drawing on network theories of psychopathology, we suggest that the cooccurrence of withdrawal symptoms may result from interactions among symptoms of withdrawal over time, rather than simply reflecting passive indicators of a latent disorder. Results indicating many associations among individual symptoms of withdrawal are consistent with a network perspective. Other results of interest include minimal changes in the network structure of withdrawal across four measurement occasions prior to and during treatment with nicotine patch and behavioral counseling.

On Standardizing Within-Person Effects: Potential Problems of Global Standardization

Person-mean centering has been recommended for disaggregating between-person and within-person effects when modeling time-varying predictors. Multilevel modeling textbooks recommended global standardization for standardizing fixed effects. An aim of this study is to evaluate whether and when person-mean centering followed by global standardization can accurately estimate fixed-effects within-person relations (the estimand of interest in this study) in multilevel modeling. We analytically derived that global standardization generally yields inconsistent (asymptotically biased) estimates for the estimand when between-person differences in within-person standard deviations exist and the average within-person relation is nonzero. Alternatively, a person-mean-SD standardization (P-S) approach yields consistent estimates. Our simulation results further revealed (1) how misleading the results from global standardization were under various circumstances and (2) the P-S approach had accurate estimates and satisfactory coverage rates of fixed-effects within-person relations when the number of occasions is 30 or more (in many conditions, performance was satisfactory with 10 or 20 occasions). A daily diary data example, focused on emotional complexity, was used to empirically illustrate the approaches. Researchers should choose standardization approaches based on theoretical considerations and should clearly describe the purpose and procedure of standardization in research articles.

Positive and Negative Emotion Regulation in Adolescence: Links to Anxiety and Depression

Emotion regulation skills develop substantially across adolescence, a period characterized by emotional challenges and developing regulatory neural circuitry. Adolescence is also a risk period for the new onset of anxiety and depressive disorders, psychopathologies which have long been associated with disruptions in regulation of positive and negative emotions. This paper reviews the current understanding of the role of disrupted emotion regulation in adolescent anxiety and depression, describing findings from self-report, behavioral, peripheral psychophysiological, and neural measures. Self-report studies robustly identified associations between emotion dysregulation and adolescent anxiety and depression. Findings from behavioral and psychophysiological studies are mixed, with some suggestion of specific impairments in reappraisal in anxiety. Results from neuroimaging studies broadly implicate altered functioning of amygdala-prefrontal cortical circuitries, although again, findings are mixed regarding specific patterns of altered neural functioning. Future work may benefit from focusing on designs that contrast effects of specific regulatory strategies, and isolate changes in emotional regulation from emotional reactivity. Approaches to improve treatments based on empirical evidence of disrupted emotion regulation in adolescents are also discussed. Future intervention studies might consider training and measurement of specific strategies in adolescents to better understand the role of emotion regulation as a treatment mechanism.

Digital phenotyping for psychiatry: Accommodating data and theory with network science methodologies

Digital phenotyping is the moment-by-moment quantification of our interactions with digital devices. With appropriate tools, digital phenotyping data afford unprecedented insight into our transactions with the world and hold promise for developing novel signatures of psychopathology that will aid in diagnosis, prognosis, and treatment selection of psychiatric disorders. In this review, we highlight empirical work merging digital phenotyping data, and particularly experience-sampling data collected via smartphone, with network theories of psychopathology and network science methodologies. The intensive, longitudinal, and multivariate data collected through digital phenotyping designs provide the necessary foundation for the application of network science methodologies to parsimoniously test network theories of psychopathology emphasizing causal interactions among psychiatric symptoms, as well as other phenotypes, across time.

Socioemotional Dynamics of Emotion Regulation and Depressive Symptoms: A Person-Specific Network Approach

Socioemotional processes engaged in daily life may afford and/or constrain individuals' emotion regulation in ways that affect psychological health. Recent findings from experience sampling studies suggest that persistence of negative emotions (emotion inertia), the strength of relations among an individual's negative emotions (density of the emotion network), and cycles of negative/aggressive interpersonal transactions are related to psychological health. Using multiple bursts of intensive experience sampling data obtained from 150 persons over one year, person-specific analysis, and impulse response analysis, this study quantifies the complex and interconnected socioemotional processes that surround individuals' daily social interactions and on-going regulation of negative emotion in terms of recovery time. We also examine how this measure of regulatory inefficiency is related to interindividual differences and intraindividual change in level of depressive symptoms. Individuals with longer recovery times had higher overall level of depressive symptoms. As well, during periods where recovery time of sadness was longer than usual, individuals' depressive symptoms were also higher than usual, particularly among individuals who experienced higher overall level of stressful life events. The findings and analysis highlight the utility of a person-specific network approach to study emotion regulation, how regulatory processes change over time, and potentially how planned changes in the configuration of individuals' systems may contribute to psychological health.

The Promise and Challenges of Intensive Longitudinal Designs for Imbalance Models of Adolescent Substance Use

Imbalance models of adolescent brain development attribute the increasing engagement in substance use during adolescence to within-person changes in the functional balance between the neural systems underlying socio-emotional, incentive processing, and cognitive control. However, the experimental designs and analytic techniques used to date do not lend themselves to explicit tests of how within-person change and within-person variability in socio-emotional processing and cognitive control place individual adolescents at risk for substance use. For a more complete articulation and a more stringent test of these models, we highlight the promise and challenges of using intensive longitudinal designs and analysis techniques that encompass many (often >10) within-person measurement occasions. Use of intensive longitudinal designs will lend researchers the tools required to make within-person inferences in individual adolescents that will ultimately align imbalance models of adolescent substance use with the methodological frameworks used to test them.