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

Momentary effects of life stressors on mindfulness and emotion regulation difficulties among adolescents exposed to chronic stressors

Adolescents faced with chronic stressors (e.g., financial instability, interpersonal violence) are at heightened risk for developing mental health problems, likely due in part to stressors that interfere with effective emotion regulation. Although mindfulness may help to act as a buffer against the deleterious effects of life stressors, a relatively untested assumption is that adolescents can maintain mindfulness during periods of stress. This paper explores this assumption by investigating the real-time, dynamic relationships among life stressors, mindfulness, and emotion regulation difficulties among adolescents exposed to chronic stressors. Eighty-one participants who were 10-18 years old (M = 14.33; SD = 2.20; 56% male; 57% Non-Hispanic White) completed ecological momentary assessments (EMA) three times a day for 7 days and contributed a total of 1186 EMA reports. Multilevel structural equation modelling revealed that the presence (vs. absence) of stressors was associated with lower momentary mindfulness and greater momentary emotion regulation difficulties concurrently and prospectively. Stressors with greater severity were also concurrently, but not prospectively, associated with lower momentary mindfulness and greater momentary emotion regulation difficulties. Findings highlight that exposure to life stressors may degrade momentary mindfulness and emotion regulation. Given that mindfulness and emotion regulation are closely associated with mental health, these results also demonstrate one way that stressors may contribute to health disparities at the micro-level. Going forward, it will be important to investigate methods of helping adolescents learn to maintain mindfulness and adaptive emotion regulation in the face of stressful events. This study was preregistered (NCT04927286).

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

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.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

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.

Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

BACKGROUND

Short-term environmental exposures, including green space, air pollution, and noise, have been suggested to affect health. However, the evidence is limited to aggregated exposure estimates which do not allow the capture of daily spatiotemporal exposure sequences. We aimed to (1) determine individuals' sequential exposure patterns along their daily mobility paths and (2) examine whether and to what extent these exposure patterns were associated with anxiety symptoms.

METHODS

We cross-sectionally tracked 141 participants aged 18-65 using their global positioning system (GPS) enabled smartphones for up to 7 days in the Netherlands. We estimated their location-dependent exposures for green space, fine particulate matter, and noise along their moving trajectories at 10-min intervals. The resulting time-resolved exposure sequences were then partitioned using multivariate time series clustering with dynamic time warping as the similarity measure. Respondents' anxiety symptoms were assessed with the Generalized Anxiety Disorders-7 questionnaire. We fitted linear regressions to assess the associations between sequential exposure patterns and anxiety symptoms.

RESULTS

We found four distinctive daily sequential exposure patterns across the participants. Exposure patterns differed in terms of exposure levels and daily variations. Regression results revealed that participants with a "moderately health-threatening" exposure pattern were significantly associated with fewer anxiety symptoms than participants with a "strongly health-threatening" exposure pattern.

CONCLUSIONS

Our findings support that environmental exposures' daily sequence and short-term magnitudes may be associated with mental health. We urge more time-resolved mobility-based assessments in future analyses of environmental health effects in daily life.

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.

Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones

Together, data from brain scanners and smartphones have sufficient coverage of biology, psychology, and environment to articulate between-person differences in the interplay within and across biological, psychological, and environmental systems thought to underlie psychopathology. An important next step is to develop frameworks that combine these two modalities in ways that leverage their coverage across layers of human experience to have maximum impact on our understanding and treatment of psychopathology. We review literature published in the last 3 years highlighting how scanners and smartphones have been combined to date, outline and discuss the strengths and weaknesses of existing approaches, and sketch a network science framework heretofore underrepresented in work combining scanners and smartphones that can push forward our understanding of health and disease.

The roles of caregivers and friends in adolescent daily emotion dynamics

Daily emotion dynamics provide valuable information about individuals' emotion processes as they go about their lives. Emotion dynamics such as emotion levels (mean), emotion variability (degree of fluctuation), and emotion network density (strength of temporal connections among emotions) are associated with risks for various psychopathology in youth and adults. Prior work has shown that caregivers and friends play crucial socializing roles in adolescent emotional well-being, but less is known about their roles in daily emotion dynamics. This study examined whether caregiver emotion coaching, caregiver-adolescent closeness, and friendship quality were associated with adolescents' emotion levels, emotion variability, and emotion network density. Further, we examined whether caregiver-adolescent closeness moderated the associations between coaching and emotion dynamics. Participants were 150 adolescents (61% girls; Mage = 14.75) and one of their caregivers (95% female; Mage = 43.35) who completed a baseline survey and 21 daily surveys. Results showed that caregiver emotion coaching interacted with caregiver-adolescent closeness in predicting emotion levels and variability. Specifically, when closeness was higher, emotion coaching was significantly associated with lower sadness and anger levels, higher happiness levels, and lower happiness variability. Caregiver emotion coaching, independent of closeness, was also associated with lower anxiety levels, lower sadness variability, and lower emotion network density. Friendship quality was significantly associated with lower levels of sadness, anxiety, and anger, higher levels of happiness, and lower variability in anxiety and anger. These findings suggest that caregivers and friends are central to everyday emotion levels and variability and a more flexible emotion system in adolescents.

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.

mHealth and technology innovations for anxiety and OC spectrum disorders

Interdisciplinary mobile health (mHealth) technologies and intervention approaches are changing the nature of health research, providing the opportunity to shift from more reactive approaches for patient care to a more proactive stance. As with the larger field of medicine, mHealth and technology-enhanced approaches in psychiatry and clinical psychology are opening an unparalleled number of avenues to help reduce the risk for psychiatric disease, treat psychological disorders, and increase well-being of our patients. While promising, this research is characterized by complex challenges across the domains of concept development, initial design and testing, and downstream implementation and scaled-up dissemination. This Special Issue in the British Journal of Clinical Psychology was designed to highlight the development and implementation of mHealth research in the anxiety and obsessive-compulsive spectrum disorders. In addition to informing readers about important advances that have been made, the present special issue also draws attention to the myriad challenges that will need to be considered in future research. Three domains relevant for mHealth research are addressed, including a careful consideration of where the research currently stands and what challenges we should prepare for, the adaptation of traditional and adjunctive treatments to mobile or online platforms, and the ability for technology and associated methodological approaches to provide further insight into aetiological investigations.