Testing the reciprocal relationship between depressive symptoms and insomnia.

BACKGROUND: Although the importance of sleep difficulties in emotional disorders has long been acknowledged, the nature of the potential reciprocal relationship between sleep and depressive symptoms is not yet well understood. The coronavirus disease 19 pandemic provided a unique opportunity to study the interrelation of these symptoms over a period marked by increases in sleep and psychological difficulties. METHODS: Using online data collection, we followed 1200 adults (59% male) through 1 year of the pandemic. Measures of sleep disturbance and depressive symptoms were assessed at eight time points. Factor analysis of the items from these two measures suggested separate insomnia and depressive symptom factors. A random intercept cross-lagged panel model was used to assess within-person relationships between factor analysis-informed subscales of insomnia and depressive symptoms. RESULTS: Depressive symptoms predicted subsequent changes in insomnia symptoms; however, insomnia did not predict changes in depressive symptoms. CONCLUSIONS: Our findings suggest the impact of depressive symptoms on insomnia is evident, but the reverse is not. Implications of this finding along with the need for research addressing depressive symptoms and insomnia as treatment targets are discussed.

Promoting skill use in skill enhanced cognitive behavioral therapy: A case example.

Cognitive behavioral therapy (CBT) appears to achieve its effects at least in part by fostering the development of CBT skills. In an effort to leverage CBT skill development, our group developed and tested a skill-enhanced version of CBT (CBT-SE) in a recent trial. In this paper, we describe our work with a client who participated in a 12-week course of CBT-SE as part of that trial. Although homework is a critical aspect of CBT, the greater emphasis on skill development in CBT-SE means that homework is even more central. This client's course of treatment illustrates the potential benefits of a strong focus on skill development and the use of specific homework assignments to foster mastery and ongoing use of CBT skills. The client developed CBT skills at a rapid pace early in treatment and exhibited enduring symptom reductions. The experience of this client reinforces the value of a focus on CBT skills and highlights strategies for fostering skill development. Given the evidence in support of the therapeutic value of CBT skills and the well-established benefits of homework assignment, we encourage use of skill enhancing procedures, including in-session procedures and assignments that help clients develop and maintain ongoing skill use.

Dynamics of diurnal cortisol and alpha-amylase secretion and their associations with PTSD onset in recent interpersonal trauma survivors.

BACKGROUND: Dysfunction in major stress response systems during the acute aftermath of trauma may contribute to risk for developing posttraumatic stress disorder (PTSD). The current study investigated how PTSD diagnosis and symptom severity, depressive symptoms, and childhood trauma uniquely relate to diurnal neuroendocrine secretion (cortisol and alpha-amylase rhythms) in women who recently experienced interpersonal trauma compared to non-traumatized controls (NTCs). METHOD: Using a longitudinal design, we examined diurnal cortisol and alpha-amylase rhythms in 98 young women (n = 57 exposed to recent interpersonal trauma, n = 41 NTCs). Participants provided saliva samples and completed symptom measures at baseline and 1-, 3-, and 6-month follow-up. RESULTS: Multilevel models (MLMs) revealed lower waking cortisol predicted the development of PTSD in trauma survivors and distinguished at-risk women from NTCs. Women with greater childhood trauma exposure exhibited flatter diurnal cortisol slopes. Among trauma-exposed individuals, lower waking cortisol levels were associated with higher concurrent PTSD symptom severity. Regarding alpha-amylase, MLMs revealed women with greater childhood trauma exposure exhibited higher waking alpha-amylase and slower diurnal alpha-amylase increase. CONCLUSIONS: Results suggest lower waking cortisol in the acute aftermath of trauma may be implicated in PTSD onset and maintenance. Findings also suggest childhood trauma may predict a different pattern of dysfunction in stress response systems following subsequent trauma exposure than the stress system dynamics associated with PTSD risk; childhood trauma appears to be associated with flattened diurnal cortisol and alpha-amylase slopes, as well as higher waking alpha-amylase.

Predicting anxiety and depression over 12 months of the COVID-19 pandemic: A machine learning study.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic was associated with substantial increases in anxiety and depressive symptoms. To understand individual risk, we examined a large set of potential risk factors for anxiety and depression in the pandemic context. METHODS: Adults in the United States (N = 1200) completed eight online self-report assessments over 12 months of the COVID-19 pandemic. Area under the curve scores summarized cumulative experiences of anxiety and depression over the assessment period. A machine learning approach to elastic net regularized regression was used to select predictors of cumulative anxiety and depression severity from a set of 68 sociodemographic, psychological, and pandemic-related baseline variables. RESULTS: Cumulative anxiety severity was most strongly explained by stress and depression-related variables (such as perceived stress) and select sociodemographic characteristics. Cumulative depression severity was predicted by psychological variables, including generalized anxiety and depressive symptom reactivity. Being immunocompromised or having a medical condition were also important. CONCLUSIONS: By considering many predictors, findings provide a more complete view than previous studies focused on specific predictors. Important predictors included psychological variables suggested by prior research and variables more specific to the pandemic context. We discuss how such findings can be used in understanding risk and planning interventions.

I've seen enough! Prolonged and repeated exposure to disgusting stimuli increases oculomotor avoidance.

Disgust motivates avoidance of stimuli associated with pathogens. Although disgust primarily inhibits oral and epidermal contact, it may also inhibit perceptual contact, particularly given the outsize role of sensory qualities in eliciting disgust. To examine perceptual avoidance of disgust, we presented images of bodily products or spoiled food paired with neutral images for 12-s trials and recorded eye movements (Experiment 1; N = 127). We found that, overall, these disgusting images were not visually avoided compared to neutral images. However, viewing of disgusting images decreased with prolonged (within-trial) and repeated (between-trial) exposure, and these trends were predicted by self-reported disgust to the images. In Experiment 2 (N = 84), we replicated Experiment 1 with a novel set of disgusting images, as well as other unpleasant image categories (suicide, threat) and pleasant images. We found that disgusting stimuli were viewed less than the other unpleasant image categories, and we again found that viewing of disgusting images decreased with prolonged and repeated exposure. Further, we replicated the finding that disgust ratings predicted decreasing viewing of disgusting images, but only for prolonged exposure (within-trial). Unexpectedly, we found that disgust ratings predicted a similar pattern of decreasing viewing for the suicide and threat images as well. These findings suggest that disgust inhibits perceptual contact, but in competition with motivational processes that steer attention toward pathogen threats. We discuss the implications for measuring disgust with eye tracking. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Predicting Posttraumatic Stress Disorder Among Survivors of Recent Interpersonal Violence.

A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.

Longitudinal Associations Among Negative Cognitions and Depressive and Posttraumatic Stress Symptoms in Women Recently Exposed to Stalking.

Being stalked is a potentially traumatic experience associated with a threat to personal safety. Although major depression and posttraumatic stress (PTS) disorder are highly prevalent among stalking victims, little is known about factors associated with risk for the onset and maintenance of depressive and PTS symptoms in individuals with recent stalking exposure. The aim of this study was to determine the role of cognitive appraisals (negative views about the self, negative views about the world, self-blame) in the development of depressive and PTS symptoms in young adult women who had experienced stalking within 1 month of their baseline assessment. Participants (n = 82) completed self-report online surveys of posttraumatic cognitions and symptoms four times over the course of 3 months. Levels of posttraumatic cognitions among female stalking victims were comparable to those in other studies of trauma-exposed individuals. Multilevel models (MLMs) revealed that within-person changes in cognitions were differentially associated with concurrent changes in depressive and PTS symptoms over time, controlling for the influence of time, age, race, ethnicity, lifetime stalking victimization, childhood trauma exposure, and symptoms of the other disorder. Whereas more negative cognitions about the world were associated with higher levels of concurrent depressive and PTS symptoms, negative cognitions about the self were uniquely associated with higher concurrent depressive symptoms. Contrary to expectation, self-blame was not significantly associated with depressive or PTS symptoms. Results provided support for the potential utility of negative cognitions as risk markers for depressive and PTS symptoms in young adult female stalking victims. The present findings suggest that interventions targeting symptom-specific thinking patterns could help reduce risk for negative mental health outcomes associated with stalking victimization.

Adhering to COVID-19 health guidelines: Examining demographic and psychological predictors of adherence.

The effort to limit the spread of the coronavirus (COVID-19) has relied heavily on the general public's compliance with health guidelines limiting social contact and mitigating risk when contact occurs. The aim of this study was to identify latent variables underlying adherence to COVID-19 guidelines and to examine demographic and psychological predictors of adherence. A sample of US adults (N = 1,200) were surveyed in late April to mid-May 2020. The factor structure of adherence was examined using exploratory factor analysis. Machine learning regression models using elastic net regularization were used to examine predictors of adherence. Two factors characterized adherence: avoidance and cleaning. Elastic net models identified differential demographic and psychological predictors of these two forms of adherence. Religious affiliation, denial coping, full-time employment, substance use coping, and being 60 or older predicted lower avoidance adherence. Behavioral and mindfulness emotion regulation skills, agreeableness, and Democrat political affiliation predicted greater avoidance adherence. For cleaning adherence, interpersonal and behavioral emotion regulation skills and conscientiousness emerged as strong predictors of greater cleaning. Efforts to promote compliance with COVID-19 health guidelines may benefit from distinguishing avoidance and cleaning adherence and considering predictors of each of these aspects of adherence.

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

Pain in the Acute Aftermath of Stalking: Associations With Posttraumatic Stress Symptoms, Depressive Symptoms, and Posttraumatic Cognitions.

This longitudinal study examined whether posttraumatic stress and depressive symptoms, posttraumatic cognitions, and ongoing cyberstalking exposures were independently associated with changes in pain outcomes among 82 young adult women with recent exposure to stalking. Multilevel models indicated that higher sensory pain intensity and pain-related interference were associated with more negative cognitions about the self. Higher affective pain intensity was associated with higher posttraumatic stress and depressive symptoms. Cyberstalking exposures were not associated with pain intensity or pain-related interference. Results reveal persistent pain complaints in recent stalking victims and highlight distinct psychological risk factors for pain intensity and pain-related interference.

Dynamics and determinants of cortisol and alpha-amylase responses to repeated stressors in recent interpersonal trauma survivors.

BACKGROUND: Alterations in major stress response systems are present during the immediate aftermath of trauma and may play a role in determining risk for developing posttraumatic stress disorder (PTSD). However, the dynamics and determinants of stress responses during this acute recovery phase, and their relevance for longitudinal clinical course and prognosis, have yet to be fully examined. The objectives of the present study were to characterize stress response and habituation patterns to repeated social stressors in women who recently experienced interpersonal trauma and to determine the extent to which these stress responses were associated with PTSD during prospective follow-up. METHOD: This longitudinal study examined salivary cortisol and alpha-amylase and heart rate (HR) responses to repeated stressors in 98 young women (ages 18-30). Participants included women who had experienced an incident of interpersonal trauma (i.e., physical and/or sexual assault) in the three months prior to their baseline assessment (n = 58) and a comparison group of healthy, non-traumatized women (n = 40). Women completed the Trier Social Stress Test (TSST), clinical interviews to evaluate posttraumatic stress symptom severity at the baseline assessment and again at 1-, 3-, and 6-month follow-ups. RESULTS: Multilevel models revealed a pattern of robust initial cortisol TSST responses and habituation across successive TSSTs; alpha-amylase and HR responses showed no evidence of habituation across TSSTs. Among interpersonal trauma survivors, current PTSD status was associated with more pronounced cortisol responses to the first TSST. Survivors exhibited similarly blunted cortisol responses across follow-up TSSTs regardless of PTSD status, suggesting habituation of cortisol responses among survivors who developed PTSD. PTSD re-experiencing symptoms were uniquely associated with blunting of cortisol TSST responses. CONCLUSION: Findings suggest that PTSD as a diagnostic entity is meaningfully associated with cortisol responses to repeated social stressors. Social-evaluative threat is a salient form of danger for interpersonal trauma survivors. Identifying the determinants of cortisol (non)habituation to repeated social-evaluative threat among interpersonal trauma survivors could inform the development of early interventions for PTSD.

Opioid prescription rates and risk for substantiated child abuse and neglect: A Bayesian spatiotemporal analysis.

OBJECTIVES: To determine the association between opioid prescribing rates and substantiated abuse and neglect across Tennessee counties during an 11-year period. METHODS: We adopted a Bayesian spatiotemporal approach to determine the association between opioid prescribing and rates of substantiated child abuse and neglect over and above environmental and population-level covariates. Annual county-level data for Tennessee (2006-2016) included rates of substantiated child abuse and neglect, rates of drug and non-drug crime incidents, racial and Hispanic composition, per capita income, child poverty and teen birth rates, and vacant housing. RESULTS: Higher opioid prescribing rates were associated with greater risk for substantiated child abuse and neglect across Tennessee counties. Risk for substantiated child abuse and neglect was positively associated with vacant housing, child poverty, teen birth rates, and rates of both drug and non-drug criminal incidents - including stimulant arrests. Risk for substantiated child abuse and neglect was negatively associated with percentages of African Americans. CONCLUSIONS: Results underscore the importance of opioid prescribing and crime rates as independent determinants of spatial and temporal variation in risk for substantiated child abuse and neglect. Policies that regulate and reduce opioid prescribing have the potential to reduce risk for child abuse and neglect.

Connecting Child Maltreatment Risk With Crime and Neighborhood Disadvantage Across Time and Place: A Bayesian Spatiotemporal Analysis.

Child maltreatment is a major public health problem. Although maltreatment rates vary over time and are influenced by neighborhood characteristics, the unique effects of crime and disadvantage on risk are not well understood. This study utilized a Bayesian spatiotemporal approach to examine risk factors for substantiated child abuse and neglect over a 9-year period across zip codes in Davidson County, TN. Risk of child sexual and physical abuse decreased from 2008 to 2016. In contrast, risk of child neglect increased from 2011 to 2014, followed by a rapid decrease in risk. Whereas higher percentages of families living in poverty were associated with higher risk of all maltreatment subtypes, higher unemployment rates were uniquely associated with risk of child neglect. Crime rates were positively associated with risk of child physical and sexual abuse but not neglect. Results have implications for tailoring prevention strategies according to geographic area and maltreatment subtype.

County-level socioeconomic and crime risk factors for substantiated child abuse and neglect.

Rates of substantiated child abuse and neglect vary significantly across counties. Despite strong cross-sectional support for links between social-contextual characteristics and abuse and neglect, few longitudinal studies have tested relations between these risk factors and substantiated rates of abuse/neglect. The goal of this study was to identify county-level socioeconomic and crime factors associated with substantiated abuse/neglect rates over 13 years (2004-2016). Annual county-level data for Tennessee, obtained from the KIDS COUNT Data Center, included rates of substantiated child abuse and neglect, children's race and ethnicity, births to unmarried women, teen birth rate, children in families receiving Supplemental Nutrition Assistance Program (SNAP) benefits, and children in families receiving Temporary Assistance for Needy Families. Annual county-level crime report data, obtained from the Tennessee Incident Based Reporting System, included sexual offenses, non-sexual assaults, stalking incidents, thefts, property damage, and drug-related offenses. Bayesian spatio-temporal models indicated that substantiated child abuse and neglect rates were independently and positively associated with teen birth rates, percentages of births to unmarried mothers, drug-related offenses, and percentages of children receiving SNAP benefits. In contrast, substantiated child abuse and neglect rates were negatively associated with percentages of African-American youth. The findings highlighted distinct demographic, socioeconomic, and crime factors associated with substantiated child abuse and neglect rates and have the potential to enhance identification of high-risk counties that could benefit from targeted abuse and neglect prevention efforts.