Associations between the resting EEG aperiodic slope and broad domains of cognitive ability.

Recent studies suggest that the EEG aperiodic exponent (often represented as a slope in log-log space) is sensitive to individual differences in momentary cognitive skills such as selective attention and information processing speed. However, findings are mixed, and most of the studies have focused on just a narrow range of cognitive domains. This study used an archival dataset to help clarify associations between resting aperiodic features and broad domains of cognitive ability, which vary in their demands on momentary processing. Undergraduates (N = 166) of age 18-52 years completed a resting EEG session as well as a standardized, individually administered assessment of cognitive ability that included measures of processing speed, working memory, and higher-order visuospatial and verbal skills. A subsample (n = 110) also completed a computerized reaction time task with three difficulty levels. Data reduction analyses revealed strong correlations between the aperiodic offset and slope across electrodes, and a single component accounted for ~60% of variance in slopes across the scalp, in both eyes-closed and eyes-open conditions. Structural equation models did not support relations between the slope and specific domains tapping momentary processes. However, secondary analyses indicated that the eyes-open slope was related to higher overall performance, as represented by a single general ability factor. A latent reaction time variable was significantly inversely related to both eyes-closed and eyes-open resting exponents, such that faster reaction times were associated with steeper slopes. These findings support and help clarify the relation of the resting EEG exponent to individual differences in cognitive skills.

Increased Pain Variability in Patients With Chronic Pain: A Role for Pain Catastrophizing.

Pain is an inherently negative perceptual and affective experience that acts as a warning system to protect the body from injury and illness. Pain unfolds over time and is influenced by myriad factors, making it highly dynamic. Despite this, statistical measures often treat any intraindividual variability in pain ratings as noise or error. This is consequential, especially for research on chronic pain, because pain variability is associated with greater pain severity and depression. Yet, differences in pain variability between patients with chronic pain and controls in response to acute pain has not been fully examined-and it is unknown if dispositional factors such as pain catastrophizing (negative cognitive-affective response to potential or actual pain in which attention cannot be diverted away from pain) relate to pain variability. In the current study, we recruited chronic-pain patients (N = 30) and pain-free controls (N = 22) to complete a 30-second thermal pain task where they continually rated a painful thermal stimulus. To quantify pain variability and capture potential dynamics, we used both a traditional intraindividual standard deviation (iSD) metric of variability and a novel derivatives approach. For both metrics, patients with chronic pain had higher variability in their pain ratings over time, and pain catastrophizing significantly mediated this relationship. This suggests patients with chronic pain experience pain stimuli differently over time, and pain catastrophizing may account for this differential experience. PERSPECTIVE: The present study demonstrates (using multiple variability metrics) that chronic pain patients show more variability when rating experimental pain stimuli, and that pain catastrophizing helps explain this differential experience. These results provide preliminary evidence that short-term pain variability could have utility as a clinical marker in pain assessment and treatment.

Life events sometimes alter the trajectory of personality development: Effect sizes for 25 life events estimated using a large, frequently assessed sample.

OBJECTIVE: Personality changes across the life span. Life events, such as marriage, becoming a parent, and retirement, have been proposed as facilitating personality growth via the adoption of novel social roles. However, empirical evidence linking life events with personality development is sparse. Most studies have relied on few assessments separated by long time intervals and have focused on a single life event. In contrast, the content of life is composed of small, recurrent experiences (e.g., getting sick or practicing a hobby), with relatively few major events (e.g., childbirth). Small, frequently experienced life events may play an important and overlooked role in personality development. METHOD: The present study examined the extent to which 25 major and minor life events alter the trajectory of personality development in a large, frequently assessed sample (Nsample = 4904, Nassessments = 47,814, median retest interval = 35 days). RESULTS: Using a flexible analytic strategy to accommodate the repeated occurrence of life events, we found that the trajectory of personality development shifted in response to a single occurrence of some major life events (e.g., divorce), and recurrent, "minor" life experiences (e.g., one's partner doing something special). CONCLUSION: Both stark role changes and frequently reinforced minor experiences can lead to personality change.

Testing the canalization hypothesis of attachment theory: Examining within-subject variation in attachment security.

According to the canalization hypothesis of attachment theory (Bowlby, 1973), people's trajectories of attachment security should become increasingly stable and buffered against external pressures as their relationships progress. The present study aimed to examine this hypothesis within the context of romantic relationships. We analyzed longitudinal data collected from 1,741 adults who completed between three and 24 survey assessments (average number of waves analyzed = 6.79, SD = 5.31; median test-retest interval = 35 days). We modeled participants' within-person fluctuations in partner-specific security as a function of their romantic relationship length. Additionally, we examined whether attachment-related events (e.g., conflict with one's partner) predict greater within-person fluctuations in security among people involved in newer versus more established romantic relationships. Our results suggest that people in newer romantic relationships demonstrated greater fluctuations in partner-specific attachment anxiety-both generally and in reaction to attachment-related events-compared to those in well-established romantic relationships. However, neither of these trends was observed for partner-specific attachment avoidance. These results provide partial support for the canalization hypothesis but also suggest that canalization processes may be more nuanced than previously assumed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Family Lifestyles, Actions and Risk Education (FLARE) study: Protocol for a randomized controlled trial of a sun protection intervention for children of melanoma survivors.

BACKGROUND: Children of parents who had melanoma are more likely to develop skin cancer themselves owing to shared familial risks. The prevention of sunburns and promotion of sun-protective behaviors are essential to control cancer among these children. The Family Lifestyles, Actions and Risk Education (FLARE) intervention will be delivered as part of a randomized controlled trial to support parent-child collaboration to improve sun safety outcomes among children of melanoma survivors. METHODS: FLARE is a two-arm randomized controlled trial design that will recruit dyads comprised of a parent who is a melanoma survivor and their child (aged 8-17 years). Dyads will be randomized to receive FLARE or standard skin cancer prevention education, which both entail 3 telehealth sessions with an interventionist. FLARE is guided by Social-Cognitive and Protection Motivation theories to target child sun protection behaviors through parent and child perceived risk for melanoma, problem-solving skills, and development of a family skin protection action plan to promote positive modeling of sun protection behaviors. At multiple assessments through one-year post-baseline, parents and children complete surveys to assess frequency of reported child sunburns, child sun protection behaviors and melanin-induced surface skin color change, and potential mediators of intervention effects (e.g., parent-child modeling). CONCLUSION: The FLARE trial addresses the need for melanoma preventive interventions for children with familial risk for the disease. If efficacious, FLARE could help to mitigate familial risk for melanoma among these children by teaching practices which, if enacted, decrease sunburn occurrence and improve children's use of well-established sun protection strategies.

Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study.

BACKGROUND: Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. OBJECTIVE: We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. METHODS: The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. RESULTS: Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. CONCLUSIONS: We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. TRIAL REGISTRATION: ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38294.

Retention, engagement, and binge-eating outcomes: Evaluating feasibility of the Binge-Eating Genetics Initiative study.

OBJECTIVE: Using preliminary data from the Binge-Eating Genetics Initiative (BEGIN), we evaluated the feasibility of delivering an eating disorder digital app, Recovery Record, through smartphone and wearable technology for individuals with binge-type eating disorders. METHODS: Participants (n = 170; 96% female) between 18 and 45 years old with lived experience of binge-eating disorder or bulimia nervosa and current binge-eating episodes were recruited through the Recovery Record app. They were randomized into a Watch (first-generation Apple Watch + iPhone) or iPhone group; they engaged with the app over 30 days and completed baseline and endpoint surveys. Retention, engagement, and associations between severity of illness and engagement were evaluated. RESULTS: Significantly more participants in the Watch group completed the study (p = .045); this group had greater engagement than the iPhone group (p's < .05; pseudo-R2 McFadden effect size = .01-.34). Overall, binge-eating episodes, reported for the previous 28 days, were significantly reduced from baseline (mean = 12.3) to endpoint (mean = 6.4): most participants in the Watch (60%) and iPhone (66%) groups reported reduced binge-eating episodes from baseline to endpoint. There were no significant group differences across measures of binge eating. In the Watch group, participants with fewer episodes of binge eating at baseline were more engaged (p's < .05; pseudo-R2 McFadden  = .01-.02). Engagement did not significantly predict binge eating at endpoint nor change in binge-eating episodes from baseline to endpoint for both the Watch and iPhone groups. DISCUSSION: Using wearable technology alongside iPhones to deliver an eating disorder app may improve study completion and app engagement compared with using iPhones alone.

Behavioral and physiological stress responses: Within-person concordance during pregnancy.

During pregnancy, a woman's emotions can have longstanding implications for both her own and her child's health. Within-person emotional concordance refers to the simultaneous measurement of emotional responses across multiple levels of analysis. This method may provide insight into how pregnant women experience emotions in response to stress. We enrolled 162 pregnant women and assessed concordance through autonomic physiology (electrodermal activity [EDA], respiratory sinus arrhythmia [RSA]), and coded behavior (Prosocial, Flight, Displacement) during the Trier Social Stress Test-Speech. We used multilevel models to examine behavioral-physiological concordance and whether self-reported emotion dysregulation moderated these effects. Participants exhibited EDA-Prosocial concordance, suggesting that prosocial behavior may be a marker of stress. Emotion dysregulation did not moderate concordance. These findings provide novel information about behavioral coping to stress in pregnancy. Given the importance of observed behavior in the maintenance and treatment of psychopathology, these findings may provide a launchpad for future perinatal intervention research.

Do life events lead to enduring changes in adult attachment styles? A naturalistic longitudinal investigation.

Research has shown that a variety of life events are associated with changes in adult attachment styles. What is unknown, however, is the extent to which those changes are transient or enduring. To investigate this issue, we followed a sample of over 4,000 people in a multiwave longitudinal study in which people naturalistically experienced a variety of life events (e.g., starting new relationships, changing jobs, average n across events = 392). This allowed us to examine people's attachment trajectories before (Mwaves = 6.51) and after (Mwaves = 8.04) specific life events took place, for spans of time ranging from 6 months to 40 (M = 23 months). We found that half of the life events we studied were associated with immediate changes in attachment styles. However, on average, people tended to revert back to levels of security similar to those that would be expected on the basis of their preevent trajectories. Nonetheless, the average person changed in enduring ways in response to a quarter of the events we studied, suggesting that some experiences lead to enduring changes in attachment. Moreover, there were considerable individual differences in the extent to which people changed: Even in cases in which the average person did not show enduring change, there was evidence that some people became more secure and others less so in enduring ways. The ways in which people construed the events (positive vs. negative) were related to the extent to which their attachment styles changed. We discuss the implications of these findings for theoretical models of attachment dynamics. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Binge Eating Genetics Initiative (BEGIN): study protocol.

BACKGROUND: The Binge Eating Genetics Initiative (BEGIN) is a multipronged investigation examining the interplay of genomic, gut microbiota, and behavioral factors in bulimia nervosa and binge-eating disorder. METHODS: 1000 individuals who meet current diagnostic criteria for bulimia nervosa or binge-eating disorder are being recruited to collect saliva samples for genotyping, fecal sampling for microbiota characterization, and recording of 30 days of passive data and behavioral phenotyping related to eating disorders using the app Recovery Record adapted for the Apple Watch. DISCUSSION: BEGIN examines the interplay of genomic, gut microbiota, and behavioral factors to explore etiology and develop predictors of risk, course of illness, and response to treatment in bulimia nervosa and binge-eating disorder. We will optimize the richness and longitudinal structure of deep passive and active phenotypic data to lay the foundation for a personalized precision medicine approach enabling just-in-time interventions that will allow individuals to disrupt eating disorder behaviors in real time before they occur. TRIAL REGISTRATION: The ClinicalTrials.gov identifier is NCT04162574. November 14, 2019, Retrospectively Registered.

Empirical Bayes Derivative Estimates.

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25-500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.

Integrating developmental theory and methodology: Using derivatives to articulate change theories, models, and inferences.

Matching theories about growth, development, and change to appropriate statistical models can present a challenge, which can result in misuse, misinterpretation, and underutilization of different analytical approaches. We discuss the use of derivatives --- the change of a construct with respect to changes in another construct. Derivatives provide a common language linking developmental theory and statistical methods. Conceptualizing change in terms of derivatives allows precise translation of theory into method and highlights commonly overlooked models of change. A wide variety of models can be understood in terms of the level, velocity and acceleration of constructs: the 0th, 1st, and 2nd derivatives, respectively. We introduce the language of derivatives, and highlight the conceptually differing questions that can be addressed in developmental studies. A substantive example is presented to demonstrate how common and unfamiliar statistical methodology can be understood as addressing relations between differing pairs of derivatives.

Getting less of what you want: reductions in statistical power and increased bias when categorizing medication adherence data.

Medication adherence is thought to be the principal clinical predictor of positive clinical outcomes, not only for serious mental illnesses such as schizophrenia, bipolar disorder, or depression, but also for physical conditions such as diabetes. Consequently, research on medication often looks not only at medication condition (e.g., placebo, standard medication, investigative medication), but also at adherence in taking those medications within each medication condition. The percentage (or proportion) scale is one of the more frequently employed and easily interpretable measures. Patients can be 0 % adherent, 100 % adherent, or somewhere in between. For simplicity, many reported adherence analyses dichotomize or trichotomize the adherence predictor when estimating its effect on outcomes of interest. However, the methodological literature shows that the practice of categorizing continuously distributed predictors reduces statistical power at best and, at worst, can severely bias parameter estimates. This can result in inflated Type I errors (false positive acceptance of null adherence effects) or Type II errors (false negative rejection of true adherence effects). We extend the methodological literature on categorization to the construct of adherence. The measurement scale of adherence leads to a diverse family of potential distributions including uniform, n-shaped, u-shaped (i.e., bimodal), positively skewed, and negatively skewed. Using a simulation study, we generated negative, null, and positive "true" effects of adherence on simulated continuous and binary outcomes. We then estimated the adherence effect with and without categorizing the adherence variable. We show how parameter estimates and standard errors can be severely biased when categorizing adherence. The categorization of adherence is shown to cause null effects to become positive or negative depending on the distribution of the simulated adherence variable, inflating Type I errors. When the adherence effect was significantly different from zero, categorization can render the effect null, inflating Type II errors. We recommend that adherence be measured continuously and analyzed without categorization when using it as a predictor in regression models.

Trait Stress Resistance and Dynamic Stress Dissipation on Health and Well-Being: The Reservoir Model.

Daily data from the NDHWB (n = 783; age range 37-90) were analyzed to produce 'dynamic characteristic' estimates of stress input and dissipation. These were used in multi-level modeling (with age and trait stress resistance) to predict depression and health trajectories. Main effects suggest that dissipation and stress resistance predict lower depression and better health, but lower stress input was only related to lower depression. Interactions revealed that subjects with above average stress resistance had lower depression irrespective of their ability to dissipate stress, but for individuals low in trait resistance those with better stress dissipation show lower depression and better health.

The reservoir model: a differential equation model of psychological regulation.

Differential equation models can be used to describe the relationships between the current state of a system of constructs (e.g., stress) and how those constructs are changing (e.g., based on variable-like experiences). The following article describes a differential equation model based on the concept of a reservoir. With a physical reservoir, such as one for water, the level of the liquid in the reservoir at any time depends on the contributions to the reservoir (inputs) and the amount of liquid removed from the reservoir (outputs). This reservoir model might be useful for constructs such as stress, where events might "add up" over time (e.g., life stressors, inputs), but individuals simultaneously take action to "blow off steam" (e.g., engage coping resources, outputs). The reservoir model can provide descriptive statistics of the inputs that contribute to the "height" (level) of a construct and a parameter that describes a person's ability to dissipate the construct. After discussing the model, we describe a method of fitting the model as a structural equation model using latent differential equation modeling and latent distribution modeling. A simulation study is presented to examine recovery of the input distribution and output parameter. The model is then applied to the daily self-reports of negative affect and stress from a sample of older adults from the Notre Dame Longitudinal Study on Aging.

Maternal depressive symptomatology and child behavior: transactional relationship with simultaneous bidirectional coupling.

The present study investigated reciprocal relationships between adolescent mothers and their children's well-being through an analysis of the coupling relationship of mothers' depressive symptomatology and children's internalizing and externalizing behaviors. Unlike studies using discrete time analyses, the present study used dynamical systems to model time continuously, which allowed for the study of dynamic, transactional effects between members of each dyad. Findings provided evidence of coupling between maternal depressive symptoms and children's behaviors. The most robust finding was that as maternal depressive symptoms became more or less severe, children's behavior problems increased or decreased in a reciprocal manner. Results from this study extended upon theoretical contributions of such authors as Richters (1997) and Granic and Hollenstein (2003), providing empirical validation from a longitudinal study for understanding the ongoing, dynamic relationships between at-risk mothers and their children.

Resilience-as-process: negative affect, stress, and coupled dynamical systems.

Resilience is often considered both a trait and a process. The current study proposes a new way to conceptualize resilience-as-process based on dynamical systems modeling, which allows researchers to capture the process of stress management in real time. Coupled damped linear oscillator models succinctly describe daily stress and negative affect in terms of developmental forces (e.g., velocity, acceleration). Models were fit to 56-day daily response data from 42 aging adults (M(age) = 78.8 years; SD(age) = 6.6 years) to observe and understand linkages between daily stress and affect. It was speculated that individuals with greater resilience would experience stress as less coupled to changes in negative affect (less stress reactivity), and would recover their affective equilibrium more quickly following a given exogenous stressor (greater stress recovery). To identify resilience resources related to reliable interindividual differences in coupling and damping between stress and negative affect, we examined possible protective factors. Aspects of personality and social support predicted both the strength and nature of this coupling, such that higher levels of these resources resulted in greater protection from the cost to negative affect from stress, as observed in damping of negative affect and decreased coupling between systems.

Estimating Dynamical Systems: Derivative Estimation Hints From Sir Ronald A. Fisher.

The fitting of dynamical systems to psychological data offers the promise of addressing new and innovative questions about how people change over time. One method of fitting dynamical systems is to estimate the derivatives of a time series and then examine the relationships between derivatives using a differential equation model. One common approach for estimating derivatives, Local Linear Approximation (LLA), produces estimates with correlated errors. Depending on the specific differential equation model used, such correlated errors can lead to severely biased estimates of differential equation model parameters. This article shows that the fitting of dynamical systems can be improved by estimating derivatives in a manner similar to that used to fit orthogonal polynomials. Two applications using simulated data compare the proposed method and a generalized form of LLA when used to estimate derivatives and when used to estimate differential equation model parameters. A third application estimates the frequency of oscillation in observations of the monthly deaths from bronchitis, emphysema, and asthma in the United Kingdom. These data are publicly available in the statistical program R, and functions in R for the method presented are provided.

Using derivative estimates to describe intraindividual variability at multiple time scales.

The study of intraindividual variability is central to the study of individuals in psychology. Previous research has related the variance observed in repeated measurements (time series) of individuals to trait-like measures that are logically related. Intraindividual measures, such as intraindividual standard deviation or the coefficient of variation, are likely to be incomplete representations of intraindividual variability. This article shows that the study of intraindividual variability can be made more productive by examining variability of interest at specific time scales, rather than considering the variability of entire time series. Furthermore, examination of variance in observed scores may not be sufficient, because these neglect the time scale dependent relationships between observations. The current article outlines a method of using estimated derivatives to examine intraindividual variability through estimates of the variance and other distributional properties at multiple time scales. In doing so, this article encourages more nuanced discussion about intraindividual variability and highlights that variability and variance are not equivalent. An example with simulated data and an example relating variability in daily measures of negative affect to neuroticism are provided.

Mood Oscillations and Coupling Between Mood and Weather in Patients with Rapid Cycling Bipolar Disorder.

Rapid Cycling Bipolar Disorder (RCBD) outpatients completed twice-daily mood self-ratings for 3 consecutive months. These ratings were matched with local measurements of atmospheric pressure, cloud cover, and temperature. Several alternative second order differential equation models were fit to the data in which mood oscillations in RCBD were allowed to be linearly coupled with daily weather patterns. The modeling results were consistent with an account of mood regulation that included intrinsic homeostatic regulation as well as coupling between weather and mood. Models were tested first in a nomothetic method where models were fit over all individuals and fit statistics of each model compared to one another. Since substantial individual differences in intrinsic dynamics were observed, the models were next fit using an ideographic method where each individual's data were fit separately and best-fitting models identified. The best-fitting within-individual model for the largest number of individuals was also the best-fitting nomothetic model: temperature and the first derivative of temperature coupled to mood and no effect of barometric pressure or cloud cover. But this model was not the best-fitting model for all individuals, suggesting that there may be substantial individual differences in the dynamic association between weather and mood in RCBD patients. Heterogeneity in the parameters of the differential equation model of homeostatic equilibrium as well as the coupling of mood to an inherently unpredictable (i.e., nonstationary) process such as weather provide an alternative account for reported broadband frequency spectra of daily mood in RCBD.