Measuring resilience prospectively as the speed of affect recovery in daily life: a complex systems perspective on mental health

Bouncing back from emotional ups and downs: insights in emotional recovery using survival analyses of burst ESM data

Many experience sampling (ESM) studies suggested that high resilience is reflected by quickly recovering one's emotional baseline. However, former studies relied on coarse data and did not look into differential recovery from emotional ups and downs. This preregistered proof-of-concept study therefore used high-resolution data collected in 2022 to compute emotional recovery after high levels of positive versus negative emotions. Adults (N = 68) participated in a three-week ESM study with eight assessments per day, complemented by short-spaced burst assessments. Resilience was assessed at baseline (trait-level; TR) and daily (day-level; DR). Multilevel survival analyses showed that high DR predicted faster returns from negative emotions, but also delayed returns following positive emotions (exp(β) = 1.32, p = 0.006). Instead, TR did not relate to emotional recovery (exp(β) = 0.85, p = 0.067). These findings were generally robust across different sensitivity analyses. This illustrates how innovative ESM designs combined with time-to-event analyses may further our insight in emotional recovery and the timescale at which it unfolds.

The contribution of a complex systems-based approach to progressive social resilience

The use of resilience in social practice has evolved from a theoretical framework at the intersection between individuals and their social ecology. Critics argue this theory still results in policies and practices that are too individualised, with the potential for negative social consequences. This paper further critiques contemporary understanding of resilience theory and its application. It juxtaposes complex systems theory with a social inequalities oriented resilience practice. This provides a paradoxical approach. It is acknowledged that state and public policy decisions and actions can be anti-resilient, undermining community and social resilience that already exists in the form of social relationships, self-organisation and co-production. Nevertheless, collective social resilience also illustrates the potential of local and service user organisations to contribute to an overall transformational change process.

An Exploratory Study of Resilience to Stressful Life Events in Autistic Children

Background

Autistic children experience more stressful life events (SLEs) than their neurotypical peers, which are related to poor mental health outcomes in both neurotypical and autistic individuals. However, there is a lack of longitudinal research assessing the perceived impact of stressful life events on autistic children's mental health.

Method

Utilizing a novel statistical technique (Ratcliff et al., 2019), called 'area of resilience to stress events' or ARSE in R, we aimed to quantify aspects of resilience, growth, and non-resilience for 67 autistic children (6-13 years old) enrolled in a larger longitudinal study who experienced a SLE. Parents reported demographic information (e.g., child age, biological sex, household income) as well as the child's internalizing and externalizing symptoms and autism characteristics across multiple time points spaced one year apart (baseline, T2, T3, T4).

Results

There was substantial variability in the resilience process within the sample. Older children exhibited a less adaptive resilience process (i.e., higher total scaled scores or arsets). Perceived stress of the disruptive event was not correlated with resilience; however, there was a significant child age x stress severity interaction, suggesting that younger children in households that perceived the disruptive event as highly stressful exhibited more efficient resilience, or lower arsets scores, compared to other children.

Conclusions

This study introduces an innovative methodological approach to understanding the effects of stressful life events on the mental health of autistic children. Results have implications for family-based policy and practice and highlight for whom services may be most beneficial.

Daily-life stress reactivity and recovery following virtual-reality-based cognitive behavioral therapy in patients with a psychotic disorder

Introduction

Studies have consistently demonstrated increased stress sensitivity in individuals with psychosis. Since stress sensitivity may play a role in the onset and maintenance of psychosis, this could potentially be a promising target for treatment. The current study was the first to investigate whether reactivity to and recovery from daily-life stressors in psychosis change in response to treatment, namely virtual-reality-based cognitive behavioral therapy (VR-CBT).

Methods

116 patients were randomized to either VR-CBT or the waiting list control group (WL). Pre-treatment and post-treatment participants completed a diary ten times a day during six to ten days. Multilevel analyses were used to model the time-lagged effects of daily stressful events on negative affect (NA) and paranoia symptoms to examine reactivity and recovery.

Results

There was a significant difference in NA reactivity. VR-CBT showed higher NA at post-treatment compared to pre-treatment than WL (bpre=0.14; bpost=0.19 vs bpre=0.18; bpost=0.14). There was a significant difference in NA recovery and paranoia recovery between the groups at lag 1: VR-CBT showed relatively lower negative affect (bpre=0.07; bpost=-0.06) and paranoia (bpre= 0.08; bpost=-0.10) at post-treatment compared to pre-treatment than WL (bpre=0.08; bpost=0.08; bpre=0.04; bpost=0.03).

Conclusion

Negative affect and paranoia recovery improved in response to treatment. Increased NA reactivity may be explained by a decrease in safety behavior in the VR-CBT group. The discrepancy between reactivity and recovery findings may be explained by the inhibitory learning theory that suggests that an original threat reaction may not erase but can be inhibited as a consequence of exposure therapy.

Do deficits in subjective stress recovery predict subsequent stress sensitivity and symptoms in schizophrenia spectrum disorders?

High levels of stress play a crucial role in the development of psychotic symptoms, such as paranoia, and may stem in part from recovery deficits after stress exposure. However, it remains unclear whether deficient recovery causes a build-up of heightened stress levels that increases stress sensitivity and symptoms when exposed to another stressor. To test this, we investigated the effect of subjective stress recovery on the response to a subsequent stressor and paranoia. We applied two consecutive runs of the same combined physical and cognitive stressor separated by a recovery phase of 60 min in individuals with schizophrenia spectrum disorders (n = 49). We repeatedly assessed self-reported stress, negative affect, heart rate, heart rate variability, salivary cortisol, and paranoia. Recovery of self-reported stress was defined as the geometric mean of the percentage changes of self-reported stress during recovery after the first stressor, and was regressed on the response to the second stressor controlling for self-reported stress during the first stressor. Lower subjective stress recovery predicted higher levels of self-reported stress, negative affect, and paranoia in response to the second stressor. The subjective stress recovery was not predictive of the physiological stress response (heart rate, heart rate variability, or salivary cortisol). Taken together, the findings indicate that recovery deficits could contribute to high levels of self-reported stress, negative affect, and paranoia in schizophrenia spectrum disorders and that the improvement of stress recovery could be a promising approach for interventions.

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.

The Influence of Social Support on the Mental Health of Elderly Individuals in Healthy Communities with the Framework of Mental Toughness

INTRODUCTION

This work aims to explore the impacts of social support measures such as analyzing greening urban spaces and improving greenspace quality on the mental health of the elderly based on the mental resilience framework to build a healthy community.

METHODS

Through literature research and a questionnaire survey, this study designs a questionnaire including basic information, a life satisfaction scale, a positive and negative emotion scale, social support rating scale, a life orientation test-revised version, and psychological resilience scale. In order to classify the scales, machine learning (ML) approaches are applied in this work.

RESULTS

The results demonstrate great variations in the scores of the elderly with various health problems in the domain of psychological resilience (p<0.001). Social support and subjective well-being are strongly related (p<0.001). Additionally, positive correlations are observed among perceived, objective, and support use (p<0.001).

CONCLUSION

Finally, it is found that psychological resilience and social support play a significant role in mediating mental health problems in elderly individuals. Therefore, social support measures such as analyzing greening urban spaces and improving greenspace quality can be adopted to improve the mental health of the elderly to build healthy communities. On this basis, this work is of certain reference value and practical significance for the construction of healthy communities and the positive influence on the mental health of elderly individuals.

Considerations toward an epigenetic and common pathways theory of mental disorder

Psychopathology emerges from the dynamic interplay of physiological and mental processes and ecological context. It can be seen as a failure of recursive, homeostatic processes to achieve adaptive re-equilibrium. This general statement can be actualized with consideration of polygenic liability, early exposures, and multiunit (multi-"level") analysis of the psychological action and the associated physiological and neural operations, all in the context of the developmental exposome. This article begins by identifying key principles and clarifying key terms necessary to mental disorder theory. It then ventures a sketch of a model that highlights epigenetic dynamics and proposes a common pathways hypothesis toward psychopathology. An epigenetic perspective elevates the importance of developmental context and adaptive systems, particularly in early life, while opening the door to new mechanistic discovery. The key proposal is that a finite number of homeostatic biological and psychological mechanisms are shared across most risky environments (and possibly many genetic liabilities) for psychopathology. Perturbation of these mediating mechanisms leads to development of psychopathology. A focus on dynamic changes in these homeostatic mechanisms across multiple units of analysis and time points can render the problem of explaining psychopathology tractable. Key questions include the mapping of recursive processes over time, at adequate density, as mental disorders unfold across development. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.

Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches

We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.

Relationship between physical performance and perception of stress and recovery in daily life post COVID-19-An explorative study

INTRODUCTION

COVID-19 is a multi-systemic disease which can target the lungs and the cardiovascular system and can also affect parts of the brain for prolonged periods of time. Even healthy athletes without comorbidities can be psychologically affected long-term by COVID-19.

OBJECTIVE

This study aimed to investigate athletes' perceived mental stress and recovery levels in daily life, and their maximal aerobic power, at three different time points, post COVID-19.

METHODS

In total, 99 athletes (62.6% male), who had been infected by COVID-19, filled out the Recovery Stress Questionnaire for Athletes (REST-Q-Sport) and completed cardiopulmonary exercise testing (endpoint maximal aerobic power output (Pmax)) at the initial screening (t1: 4 months after infection). Follow-up assessments occurred three (t2, n = 37) and seven months after t1 (t3, n = 19).

RESULTS

Subgroup means from the Recovery category were significantly below the reference value of four at all three time points, except "General Recovery" (3.76 (± 0.96), p = 0.275, d = 0.968) at t3."Overtiredness" (2.34 (± 1.27), p = 0.020, r = 0.224) was significantly above the reference value of two at t1, while all other Stress subgroups were not significantly different from the reference value or were significantly below the maximum threshold of two at t1, t2 and t3. Spearman's ρ revealed a negative association between Pmax and the subcategories of stress (ρ = -0.54 to ρ = -0.11, p < 0.050), and positive correlations between Pmax and "Somatic Recovery" (ρ = 0.43, p < 0.001) and "General Recovery" (ρ = 0.23, p = 0.040) at t1. Pmax (t1: 3.83 (± 0.99), t2: 3.78 (± 1.14), β = 0.06, p < 0.003) increased significantly from t1 to t2. In addition, REST-Q-Sport indicated a decrease in "Sleep" (t2 = 2.35 (± 0.62), t3 = 2.28(± 0.61), β = -0.18, p < 0.023) at t3, when compared to t2.

CONCLUSION

The perceived recovery seems to be negatively affected in post COVID-19 athletes. Physical performance post COVID-19 correlates with both "Emotional and Somatic Stress" and "Somatic and General Recovery", indicating potential mental and physical benefits of exercise. While it is evident that COVID-19, like other viral infections, may have an influence on physical performance, monitoring stress and recovery perceptions of athletes is critical to facilitate their return-to-sports, while minimizing long-term COVID-19 induced negative effects like the athletic objective and subjective perceived recovery and stress levels.

Delayed affective recovery to daily-life stressors signals a risk for depression

OBJECTIVE

The aim of this study is to investigate the time to affective recovery from daily-life stressors between healthy controls (HC) and two groups with an increased risk for developing depression: individuals with subclinical symptoms of depression (SSD), and individuals remitted from a depressive episode with residual symptoms of depression (RRS).

METHOD

The experience sampling method (ESM) was used to measure affective recovery to daily-life stressors. Affective recovery was defined as the moment that negative affect (NA) returned to baseline level following the first stressful event of the day. We assessed two different operationalizations of the baseline: NA at the moment before the stressful event (t_-1), and mean-person NA. The effect of stress intensity, and cumulative stress were also assessed.

RESULTS

Survival analyses showed significantly longer recovery times for the at risk groups in comparison to healthy individuals, albeit no significant difference was found between the two at risk groups (i.e. SSD and RRS). There was also an effect of cumulative stress, but not stress intensity on time to recovery in that cumulative stress resulted in significantly longer recovery times for all three groups.

LIMITATIONS

The present study is limited by the ESM sampling design, assessments take place post-stress and therefore do not capture peak stress. Additionally, we are only able to assess patterns at the group level. Finally, there is a significant age difference between groups.

CONCLUSION

Individuals at risk for depression display a delayed recovery to daily-life stressors when compared to healthy controls, which is not explained by differences in stress intensity or cumulative stress. Understanding what is driving this delay may help combat the development of depression.

Nocturnal hypoxemia is related to morning negative affectivity in untreated patients with severe obstructive sleep apnea

The relationship between sleep apnea and morning affectivity remains unclear. We aimed to clarify how sleep disturbance in patients with obstructive sleep apnea (OSA) influences their affectivity. The enrolled participants underwent the Positive and Negative Affect Schedule on their beds immediately before and after overnight polysomnography. Thirty patients with OSA were divided into two groups according to the apnea-hypopnea index (AHI): mild to moderate OSA (5 ≤ AHI < 30/h) and severe OSA (AHI ≥ 30/h) groups. Additionally, 11 healthy participants (AHI < 5/h) were included as the control group. No independent association was found between affectivity and OSA severity markers in the whole population; however, the severe OSA group had a significantly higher cumulative percentage of sleep time at saturations < 90% (CT90) and worsened morning negative affectivity. Multiple regression analysis showed that CT90 was an independent factor for increasing negative affectivity in the severe OSA group (p = 0.0422). In patients with OSA, the receiver operating characteristic curve analysis showed that the best cutoff value for CT90 for predicting no decrease in negative affectivity after sleep was 1.0% (sensitivity = 0.56, specificity = 0.86); the corresponding area under the curve was 0.71. Worsening of negative affectivity in the morning was influenced by nocturnal hypoxemia in patients with severe OSA.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

Resilience in sports: a multidisciplinary, dynamic, and personalized perspective

Athletes are exposed to various psychological and physiological stressors, such as losing matches and high training loads. Understanding and improving the resilience of athletes is therefore crucial to prevent performance decrements and psychological or physical problems. In this review, resilience is conceptualized as a dynamic process of bouncing back to normal functioning following stressors. This process has been of wide interest in psychology, but also in the physiology and sports science literature (e.g. load and recovery). To improve our understanding of the process of resilience, we argue for a collaborative synthesis of knowledge from the domains of psychology, physiology, sports science, and data science. Accordingly, we propose a multidisciplinary, dynamic, and personalized research agenda on resilience. We explain how new technologies and data science applications are important future trends (1) to detect warning signals for resilience losses in (combinations of) psychological and physiological changes, and (2) to provide athletes and their coaches with personalized feedback about athletes' resilience.

Concepts of Resilience in Adolescent Mental Health Research

PURPOSE

Strengthening the resilience of adolescents is central to promoting long-term mental health outcomes, but resilience is a widely used term, often applied in different ways. We explored how resilience is understood in the adolescent mental health literature and developed a framework that synthesizes the core characteristics of different resilience concepts.

METHODS

Systematic searches of electronic databases, hand searches of reference lists and known work on resilience, and multidisciplinary team discussions were employed, and articles with explicit conceptualizations of resilience in adolescent mental health literature were included. After screening 9,562 articles, 105 articles were retained, analyzed and used to refine a framework.

RESULTS

Three dimensions capture the various concepts of resilience, each with two end points: (1) resilience as withstanding adversity without any distress versus overcoming the distress resulting from adversity; (2) resilience as an existing trait versus as a process over time; and (3) resilience of individuals versus resilience of groups.

CONCLUSION

Concepts of resilience in adolescence can be clearly categorized within three distinct dimensions. Referring to this framework may help to clarify and compare different concepts of resilience of adolescents in mental health research as well as at a policy level and in clinical contexts.

Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed

While the negative association between physical activity and depression has been well established, it is unclear what precise characteristics of physical activity patterns explain this association. Complexity measures may identify previously unexplored aspects of objectively measured activity patterns, such as the extent to which individuals show repetitive periods of physical activity and the diversity in durations of such repetitive activity patterns. We compared the complexity levels of actigraphy data gathered over 4 weeks ([Formula: see text] data points each) for every individual, from non-depressed ([Formula: see text]) and depressed ([Formula: see text]) groups using recurrence plots. Significantly lower levels of complexity were detected in the actigraphy data from the depressed group as compared to non-depressed controls, both in terms of lower mean durations of periods of recurrent physical activity and less diversity in the duration of these periods. Further, diagnosis of depression was not significantly associated with mean activity levels or measures of circadian rhythm stability, and predicted depression status better than these.

Modeling the onset of a depressive episode: A self-regulation perspective

Major depression is an episodic disorder which, for many individuals, has its onset in a distinct change of emotional state which then persists over time. The present article explores the utility of combining a dynamical systems approach to depression, focusing specifically on the change of state associated with episode onset, with a self-regulation perspective, which operationalizes how feedback received in the ongoing process of goal pursuit influences affect, motivation, and behavior, for understanding how a depressive episode begins. The goals of this review are to survey the recent literature modeling the onset of a depressive episode and to illustrate how a self-regulation perspective can provide a conceptual framework and testable hypotheses regarding episode onset within a dynamical systems model of depression.

Examining Response to Negative Life Events Through Fitness Tracker Data

Negative life events, such as the death of a loved one, are an unavoidable part of life. These events can be overwhelmingly stressful and may lead to the development of mental health disorders. To mitigate these adverse developments, prior literature has utilized measures of psychological responses to negative life events to better understand their effects on mental health. However, psychological changes represent only one aspect of an individual's potential response. We posit measuring additional dimensions of health, such as physical health, may also be beneficial, as physical health itself may be affected by negative life events and measuring its response could provide context to changes in mental health. Therefore, the primary aim of this work was to quantify how an individual's physical health changes in response to negative life events by testing for deviations in their physiological and behavioral state (PB-state). After capturing post-event, PB-state responses, our second aim sought to contextualize changes within known factors of psychological response to negative life events, namely coping strategies. To do so, we utilized a cohort of professionals across the United States monitored for 1 year and who experienced a negative life event while under observation. Garmin Vivosmart-3 devices provided a multidimensional representation of one's PB-state by collecting measures of resting heart rate, physical activity, and sleep. To test for deviations in PB-state following negative life events, One-Class Support Vector Machines were trained on a window of time prior to the event, which established a PB-state baseline. The model then evaluated participant's PB-state on the day of the life event and each day that followed, assigning each day a level of deviance relative to the participant's baseline. Resulting response curves were then examined in association with the use of various coping strategies using Bayesian gamma-hurdle regression models. The results from our objectives suggest that physical determinants of health also deviate in response to negative life events and that these deviations can be mitigated through different coping strategies. Taken together, these observations stress the need to examine physical determinants of health alongside psychological determinants when investigating the effects of negative life events.

Overnight affective dynamics and sleep characteristics as predictors of depression and its development in women

Study Objectives

We examined (1) differences in overnight affective inertia (carry-over of evening affect to the next morning) for positive (PA) and negative affect (NA) between individuals with past, current, and no depression; (2) how sleep duration and quality influence overnight affective inertia in these groups, and (3) whether overnight affective inertia predicts depression development.

Methods

We used data of 579 women from the East-Flanders Prospective Twin Survey. For aim 1 and 2, individuals with past (n = 82), current (n = 26), and without (lifetime) depression (n = 471) at baseline were examined. For aim 3, we examined individuals who did (n = 58) and did not (n = 319) develop a depressive episode at 12-month follow-up. Momentary PA and NA were assessed 10 times a day for 5 days. Sleep was assessed daily with sleep diaries. Affective inertia was operationalized as the influence of evening affect on morning affect. Linear mixed-effect models were used to test the hypotheses.

Results

Overnight affective inertia for NA was significantly larger in the current compared to the non-depressed group, and daytime NA inertia was larger in the past compared to the non-depressed group. Overnight NA inertia was differently associated with shorter sleep duration in both depression groups and with lower sleep quality in the current compared to the non-depressed group. Overnight affective inertia did not predict depression development at 12-month follow-up.

Conclusions

Current findings demonstrate the importance of studying complex affect dynamics such as overnight affective inertia in relation to depression and sleep characteristics. Replication of these findings, preferably with longer time-series, is needed.

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

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

Reflections on psychological resilience: a comparison of three conceptually different operationalizations in predicting mental health

BACKGROUND

Psychological resilience refers to the ability to maintain mental health or recover quickly after stress. Despite the popularity of resilience research, there is no consensus understanding or operationalization of resilience.

OBJECTIVE

We plan to compare three indicators of resilience that each involve a different operationalization of the construct: a) General resilience or one's self-reported general ability to overcome adversities; b) Daily resilience as momentarily experienced ability to overcome adversities; and c) Recovery speed evident in the pattern of negative affect recovery after small adversities in daily life. These three indicators are constructed per person to investigate their cross-sectional associations, stability over time, and predictive validity regarding mental health.

METHODS

Data will be derived from the prospective MIRORR study that comprises 96 individuals at different levels of psychosis risk and contains both single-time assessed questionnaires and 90-days intensive longitudinal data collection at baseline (T0) and three yearly follow-up waves (T1-T3). General resilience is assessed using the Brief Resilience Scale (BRS) at baseline. Daily resilience is measured by averaging daily resilience scores across 90 days. For recovery speed, vector-autoregressive models with consecutive impulse response simulations will be applied to diary data on negative affect and daily stressors to calculate pattern of affect recovery. These indicators will be correlated concurrently (at T0) to assess their overlap and prospectively (between T0 and T1) to estimate their stability. Their predictive potential will be assessed by regression analysis with mental health (SCL-90) as an outcome, resilience indicators as predictors, and stressful life events as a moderator.

CONCLUSION

The comparison of different conceptualizations of psychological resilience can increase our understanding of its multifaceted nature and, in future, help improve diagnostic, prevention and intervention strategies aimed at increasing psychological resilience.