Neural and psychological correlates of post-traumatic stress symptoms in a community adult sample.

A multitude of factors are associated with the symptoms of post-traumatic stress disorder. However, establishing which predictors are most strongly associated with post-traumatic stress disorder symptoms is complicated because few studies are able to consider multiple factors simultaneously across the biopsychosocial domains that are implicated by existing theoretical models. Further, post-traumatic stress disorder is heterogeneous, and studies using case-control designs may obscure which factors relate uniquely to symptom dimensions. Here we used Bayesian variable selection to identify the most important predictors for overall post-traumatic stress disorder symptoms and individual symptom dimensions in a community sample of 569 adults (18 to 85 yr of age). Candidate predictors were selected from previously established risk factors relevant for post-traumatic stress disorder and included psychological measures, behavioral measures, and resting state functional connectivity among brain regions. In a follow-up analysis, we compared results controlling for current depression symptoms in order to examine specificity. Poor sleep quality and dimensions of temperament and impulsivity were consistently associated with greater post-traumatic stress disorder symptom severity. In addition to self-report measures, brain functional connectivity among regions commonly ascribed to the default mode network, central executive network, and salience network explained the unique variability of post-traumatic stress disorder symptoms. This study demonstrates the unique contributions of psychological measures and neural substrates to post-traumatic stress disorder symptoms.

Intersecting Relationships of Psychosocial and Structural Syndemic Problems Among People with HIV in South Africa: Using Network Analysis to Identify Influential Problems.

In South Africa, little is known about interrelationships between syndemic problems among people with HIV (PWH). A better understanding of syndemic problems may yield important information regarding factors amenable to mitigation. We surveyed 194 PWH in Khayelitsha, outside of Cape Town, South Africa. We used network analysis to examine the frequency of 10 syndemic problems and their interrelationships. Syndemic problems among PWH in South Africa were common; 159 (82.8%) participants reported at least 2 co-occurring syndemic problems and 90 (46.9%) endorsed 4 or more. Network analysis revealed seven statistically significant associations. The most central problems were depression, substance use, and food insecurity. Three clusters of syndemics were identified: mood and violence; structural factors; and behavioral factors. Depression, substance use, and food insecurity commonly co-occur among PWH in sub-Saharan Africa and interfere with HIV outcomes. Network analysis can identify intervention targets to potentially improve HIV treatment outcomes.

RESUMEN: En Sudáfrica, poco se sabe sobre interrelaciones entre problemas sindémicos entre personas con VIH (PCV). Un major entendimiento de los problemas sindémicos puede arrojar información importante sobre los factores susceptibles de mitigación. Utilizamos el análisis de redes para examinar la frecuencia de 10 problemas sindémicos y sus interrelaciones. Problemas sindémicos entre PCV en Sudáfrica eran communes; 159 (82.8%) participantes presentaron al menos 2 problemas sindémicos concurrentes y 90 (46.9%) presentaron 4 o más. El análisis de red reveló siete asociaciones estadísticamente significativas. Los problemas más centrales fueron la depresión, el uso de sustancias y la inseguridad alimentaria. Se indetificaron tres grupos de sindemias: estado de ánimo y violencia; factores estructurales; y factores de comportamiento. La depresión, el uso de sustancias y la inseguridad alimentaria comúnmente ocurren simultáneamente entre las PCV en el África subsahariana e interfieren con los resultados del VIH. El análisis de redes puede identificar objetivos de intervención para potencialmente mejorar los resultados del tratamiento del VIH.

Perceived neighborhood factors, health behaviors, and related outcomes in the Hispanic Community Health Study/Study of Latinos.

Hispanic/Latino populations may experience significant neighborhood disadvantage, but limited research has explored whether these factors affect their health behaviors. Associations between perceived neighborhood factors at Visit 1 and health behaviors and related outcomes at Visit 2 in the Hispanic Community Health Study/Study of Latinos were evaluated. Multivariable logistic regression assessed cross-sectional and longitudinal relationships between perceived neighborhood social cohesion (NSC, 5 items), and neighborhood problems (NP, 7 items), with cancer screening, current smoking, excessive/binge drinking, hypertension, obesity, physical activity, and poor diet by gender and birthplace. NSC and NP scores were converted into quartiles. Mean age of participants was 42.5 years and 62.1% were women. Perceived NP, but not perceived NSC, differed by gender (p < 0.001). In unstratified models, no significant associations were observed between perceived NSC and any health behavior, whereas greater perceived NP was associated with less adherence to colon cancer screening (moderate level: aOR = 0.68, 95% CI = 0.51, 090) and more physical activity (very high level: aOR = 1.34, 95% CI = 1.06, 1.69) compared to low perceived NP. Women with moderate perceived NP, versus low NP, had a lower odds of colon cancer screening at Visit 1 (aOR = 0.62, 95% CI = 0.43, 0.91) and higher odds of mammogram adherence at Visit 2 (aOR = 2.86, 95% CI = 1.44, 5.68). Men with high perceived NP had a higher odds of excessive or binge drinking at Visit 2 (aOR = 1.99, 95% CI = 1.19, 3.31). We conclude that perceived NP were significantly related to health behaviors among HCHS/SOL individuals. Perceptions of neighborhood environment may be considered modifiable factors of structural neighborhood environment interventions.

Symptom-level networks of youth- and parent-reported depression and anxiety in a transdiagnostic clinical sample.

Depression and anxiety disorders confer a significant public health concern for youth and their co-occurrence places youth at a higher risk for poorer psychosocial outcomes. In the present study, we use network analysis to investigate the role of and interactions among individual depression and anxiety symptoms in a treatment-seeking clinical sample. METHODS: We estimate regularized partial correlation networks for youth- and parent reported symptoms in a transdiagnostic sample of youth (N = 417, ages 8-18). We examined features of the symptom-level networks such as network stability, centrality, bridge symptoms, and communities in both youth- and parent-reported networks. RESULTS: Results indicate stable networks with disorder-specific clustering, such that symptoms were more interconnected within compared to between disorders. Symptoms related to self-comparison to peers and negative views of the future were most central in both networks. Symptoms of depression and anxiety were connected by worries for the future and hopelessness in the youth-reported network, whereas self-comparison to peers and low self-efficacy were bridge symptoms in the parent network. Distinct symptom clusters emerged in the parent- and youth-reported networks. CONCLUSIONS: Our findings indicate that negative self-evaluation, negative views of the future, and repetitive negative thinking more generally are influential symptoms in the presentation and co-occurrence of depression and anxiety and as such may be promising targets in the treatment and prevention of depression and anxiety in youth.

Whole-Brain Functional Dynamics Track Depressive Symptom Severity.

Depressive symptoms are reported by 20% of the population and are related to altered functional integrity of large-scale brain networks. The link between moment-to-moment brain function and depressive symptomatology, and the implications of these relationships for clinical and community populations alike, remain understudied. The present study examined relationships between functional brain dynamics and subclinical-to-mild depressive symptomatology in a large community sample of adults with and without psychiatric diagnoses. This study used data made available through the Enhanced Nathan Kline Institute-Rockland Sample; 445 participants between 18 and 65 years of age completed a 10-min resting-state functional MRI scan. Coactivation pattern analysis was used to examine the dimensional relationship between depressive symptoms and whole-brain states. Elevated levels of depressive symptoms were associated with increased frequency and dwell time of the default mode network, a brain network associated with self-referential thought, evaluative judgment, and social cognition. Furthermore, increased depressive symptom severity was associated with less frequent occurrences of a hybrid brain network implicated in cognitive control and goal-directed behavior, which may impair the inhibition of negative thinking patterns in depressed individuals. These findings demonstrate how temporally dynamic techniques offer novel insights into time-varying neural processes underlying subclinical and clinically meaningful depressive symptomatology.

Elucidating mechanisms of quality-of-life disparities in Hispanic women with breast cancer: An examination of disease stage, coping, and affect.

OBJECTIVES: Compared to non-Hispanic white (NHW) women, Hispanic women with breast cancer (BCa) are more likely to be diagnosed at later stages of disease and experience reduced quality of life (QOL) following diagnosis. We hypothesized that the demands of later-stage disease results in a perceived inability to cope and greater distress for Hispanic women, resulting in decreased QOL. METHODS: Hispanic (51%) and NHW (49%) women (N = 198) with newly diagnosed stage 0-3 BCa in Miami were enrolled in two trials between 2006 and 2019. In this cross-sectional analysis, a multiple-group structural equation modeling approach was applied to baseline measures of coping confidence (Measure of Current Status Scale), negative and positive affect (Affect Balance Scale), QOL (Functional Assessment of Cancer Therapy - Breast), and disease stage. RESULTS: In our model, later-stage disease was not associated with worse QOL for Hispanic or NHW women. However, there were differences between Hispanic and NHW women on the path from disease stage to (1) coping confidence, (2) positive affect, and (3) negative affect, such that later disease stage was associated with lower coping confidence (b[SE] = -1.75[0.59], p = 0.002), less positive affect (b[SE] = -0.21[0.10], p = 0.026), and greater negative affect (b[SE] = 0.15[0.08], p = 0.052) among Hispanic, but not NHW, women. In addition, an indirect effect was found from greater stage to poorer QOL via less positive affect among Hispanic women only (b[SE] = -0.49[0.24], p = 0.041). CONCLUSIONS: This data supports our theory that Hispanic women experience worse emotional distress at later-stage disease than do NHW women, in turn impacting QOL.

Factors associated with barriers to engagement in HIV-prevention care among sexual minority men.

Engagement with HIV-prevention services varies among sexual minority men (SMM). 183 HIV-negative SMM completed a baseline assessment including sociodemographic, psychosocial, identity and stigma, and sexuality measures, as well as HIV-prevention behaviors: (1) date of most recent HIV test, (2) whether they discussed HIV or (3) sexual behavior with their provider, and (4) disclosure to provider about engaging in condomless anal sex (CAS). Factor analysis of these four items yielded an HIV-prevention engagement factor score. Stochastic search variable selection (SSVS) followed by multiple linear regression identified variables associated with HIV-prevention engagement. SSVS identified three variables for inclusion in a multiple linear regression model. Not disclosing sexual orientation to one's provider (p < 0.001), discomfort discussing sex with provider (p < 0.001) and lower education (p = 0.007) were associated with less HIV-prevention engagement. Findings suggest the importance of training providers in culturally competent care to mitigate observed barriers.

Insomnia, Health, and Health-related Quality of Life in an Urban Clinic Sample of People Living with HIV/AIDS.

OBJECTIVE/BACKGROUND: Insomnia is a prevalent and interfering comorbidity of HIV infection. Nearly 70% of people living with HIV/AIDS (PLWHA) experience symptoms of insomnia and associated distress. The current study examined the mechanisms of insomnia in HIV health status and health-related quality of life and explored behavioral pathways to explain relationships. PARTICIPANTS: Participants (N = 103) were active patients in an HIV clinic located within a nonprofit, tertiary care hospital in a large, urban city in the Southeast United States. METHODS: Participants completed a clinical sleep interview and self-report assessments for adherence to antiretroviral medication, depression (PHQ-9), quality of life (ACTG-QOL), and relevant covariates. Viral load and CD4 were obtained via medical chart review. RESULTS: Insomnia affected 67% of the clinic sample. Insomnia symptoms were directly associated with poorer health-related quality of life (p<.001). Greater insomnia symptoms were also significantly associated with greater depressive symptoms [b =.495, S.E. =.061], poorer medication adherence [b = -.912, S.E. =.292], and worse health status measured by absolute CD4 count [b = -.011, S.E. =.005]. CONCLUSIONS: In this sample of PLWHA, insomnia was associated with poorer health-related quality of life and worse health status. Future research and practice should consider insomnia treatment for this population, as it could improve overall health and well-being.

Modeling daily and weekly moderate and vigorous physical activity using zero-inflated mixture Poisson distribution.

Recently developed accelerometer devices have been used in large epidemiological studies for continuous and objective monitoring of physical activities. Typically, physical movements are summarized as minutes in light, moderate, and vigorous physical activities in each wearing day. Because of preponderance of zeros, zero-inflated distributions have been used for modeling the daily moderate or higher levels of physical activity. Yet, these models do not fully account for variations in daily physical activity and cannot be extended to model weekly physical activity explicitly, while the weekly physical activity is considered as an indicator for a subject's average level of physical activity. To overcome these limitations, we propose to use a zero-inflated Poisson mixture distribution that can model daily and weekly physical activity in same family of mixture distributions. Under this method, the likelihood of an inactive day and the amount of exercise in an active day are simultaneously modeled by a joint random effects model to incorporate heterogeneity across participants. If needed, the method has the flexibility to include an additional random effect to address extra variations in daily physical activity. Maximum likelihood estimation can be obtained through Gaussian quadrature technique, which is implemented conveniently in an R package GLMMadaptive. Method performances are examined using simulation studies. The method is applied to data from the Hispanic Community Health Study/Study of Latinos to examine the relationship between physical activity and BMI groups and within a participant the difference in physical activity between weekends and weekdays.

Examining a Syndemics Network Among Young Latino Men Who Have Sex with Men.

BACKGROUND: Although studies consistently find that syndemic indicators are additively associated with increased HIV/STI risk behavior (e.g., condomless anal sex; CAS) among men who have sex with men (MSM), information is lacking about how syndemic indicators are associated with each other. Young Latino MSM are one of the most at-risk groups for acquiring HIV in the U.S. Understanding the associations of syndemic indicators with each other and with CAS may improve understanding of how to enhance sexual and behavioral health in this population. METHOD: Network analysis using the graphical LASSO (glasso) algorithm was employed to explore associations between CAS and syndemic indicators among 139 young Latino MSM. Structural and psychosocial syndemic indicators were assessed via self-report. CAS was defined as the number of partners in the past 3 months with whom one engaged in CAS. RESULTS: Results of the network analysis suggested the variables with the highest centrality were unstable housing, prison history, childhood sexual abuse, and CAS. Specific significant associations included links between CAS and alcohol use (b = 0.40), childhood sexual abuse and unstable housing (b = - 0.75), alcohol use and childhood sexual abuse (b = 0.40), and substance use and intimate partner violence (b = 0.43). CONCLUSION: This pattern of interconnectedness demonstrates the potential for network analysis to examine nuanced interrelationships of syndemic indicators. The specific associations in this sample raise the question whether a primary focus of interventions should address the more central syndemic indicators for this population, such as alcohol use and unstable housing, and whether this would, via downstream effects, affect other aspects of behavioral health in this population.

Child eyewitness researchers often bin age: Prevalence of the practice and recommendations for analyzing developmental trends.

OBJECTIVE: Effective practices for eliciting and analyzing children's eyewitness reports rely on accurate conclusions about age differences in how children retain information and respond to memory probes. Binning, which is the practice of categorizing continuous variables into discrete groups, can lower studies' power to detect age differences and, in some situations, produce significant but spurious effects. In this article, we (a) describe a systematic review that estimated the frequency of binning age in child eyewitness studies, (b) analyze real and simulated data to illustrate how binning can distort conclusions about age and covariate effects, and (c) demonstrate best practices for analyzing and reporting age trends. HYPOTHESES: We expected that researchers would frequently bin age and that we would replicate the negative consequences of binning in the demonstration data sets. METHOD: For the systematic review, we retrieved 58 articles describing child eyewitness studies and determined whether researchers binned age for one randomly selected analysis per article. We then compared alternative ways of analyzing actual and simulated data sets. RESULTS: Researchers binned age for 64% of the analyses (88% of analyses involving experimental manipulations vs. 35% of the nonexperimental analyses, φ = .55, p < .01). A significant age trend in the real data example was nonsignificant when age was treated as categorical, and in the simulated data sets we demonstrate how this practice may lead to detecting a spurious effect. CONCLUSIONS: Treating age as a continuous variable maximizes power to detect real differences without inflating the frequency of spurious results, thereby ensuring that policies regarding child eyewitnesses reflect developmental changes in children's needs and abilities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The situation or the person? Individual and task-evoked differences in BOLD activity.

Investigations of between-person variability are enjoying a recent resurgence in functional magnetic resonance imaging (fMRI) research. Several recent studies have found persistent between-person differences in blood-oxygenated-level dependent (BOLD) activation patterns and resting-state functional connectivity. Conflicting findings have been reported regarding the extent to which (a) between-person or (b) within-person cognitive state differences explain differences in BOLD activation patterns. These discrepancies may arise due to statistical analysis choices, parcellation resolution, and limited sampling of task-fMRI datasets. We attempt to address these issues in a large-scale analysis of several task-fMRI paradigms. Using a novel application of multivariate distance matrix regression, we examine between-person and task-condition variability estimates across varying levels of "resolution", from a coarse region-of-interest level to the vertex-level, and across different distance metrics. These analyses revealed that under most circumstances, differences in task conditions explained a greater amount of variance in activation map differences than between-person differences. However, this finding was reversed when comparing activation maps at a "high-resolution" vertex level. More generally, we observed that when moving from "low" to "high" resolutions, the variance explained by between-person differences increased while variance explained by task conditions decreased. We further analyzed the relationships among subject-level activation maps across all task-conditions using an unsupervised clustering approach and identified a superordinate task structure. This structure went beyond conventional task labels and highlighted those experimental manipulations across task conditions that produce contrasting versus similar whole-brain activation patterns. Overall, these analyses suggest that the question of the subject- versus task-effects on BOLD activation patterns is nontrivial, and depends on the comparison "resolution," choice of distance metric, and the coding of task-conditions.

Longitudinal Effects of Syndemics on HIV-Positive Sexual Minority Men's Sexual Health Behaviors.

This study examined the longitudinal effects of co-occurring psychosocial concerns, or syndemics, on HIV-positive sexual minority men's likelihood of engaging in serodiscordant condomless anal sex (CAS), a health behavior with implications for personal and public health. Participants included 390 HIV-positive sexual minority men from two prior secondary prevention trials. Over the course of the 1-year data collection period (up to 5 observations per participant), participants completed self-report measures of CAS, as well as six syndemic factors: post-traumatic stress disorder, childhood sexual abuse, depression, anxiety, alcohol abuse, and polysubstance/stimulant use. We employed multilevel modeling to examine the longitudinal additive effect of syndemics on serodiscordant CAS (binary) over the 1-year period. The number of syndemic conditions was a significant predictor of CAS, with each additional syndemic associated with 1.41 greater odds of CAS (p = .0004; 95% CI [1.16, 1.70]). Both the between-person (p = .0121, 95% CI [1.07, 1.69]) and within-person (p = .01, 95% CI [1.11, 2.10]) effects of syndemics were significant predictors, showing that an increase in the number of syndemic conditions across person and time both increased odds of CAS. Interventions addressing HIV-positive sexual minority men's sexual health behaviors should address the potential impact of co-occurring psychosocial concerns that affect these behaviors. This will benefit this population's personal sexual health and reduce transmission of HIV and STIs among sexual minority men.

Longitudinal Effects of Syndemics on ART Non-adherence Among Sexual Minority Men.

This study examined longitudinally the additive effect of syndemics, or co-occurring psychosocial problems, on antiretroviral treatment (ART) non-adherence among 390 HIV-positive sexual minority men. Participants completed measures of ART adherence (reduced to a non-adherence score using exploratory factor analysis) and six syndemic conditions. We employed multilevel modeling with the number of syndemics as a longitudinal predictor of non-adherence, and logistic regression with baseline syndemics predicting follow up viral load. Number of syndemics was a significant longitudinal predictor of non-adherence, with each additional syndemic associated with a 0.13 increase in non-adherence (p = 0.004). Each additional syndemic was also associated with 1.27 greater odds of detectable viral load (p = 0.002). Among HIV-positive sexual minority men in this sample, more syndemics were associated with lower ART adherence and greater odds of detectable viral load, suggesting the need for behavioral intervention to facilitate care for this population.

Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators.

Psychometric models for item-level data are broadly useful in psychology. A recurring issue for estimating item factor analysis (IFA) models is low-item endorsement (item sparseness), due to limited sample sizes or extreme items such as rare symptoms or behaviors. In this paper, I demonstrate that under conditions characterized by sparseness, currently available estimation methods, including maximum likelihood (ML), are likely to fail to converge or lead to extreme estimates and low empirical power. Bayesian estimation incorporating prior information is a promising alternative to ML estimation for IFA models with item sparseness. In this article, I use a simulation study to demonstrate that Bayesian estimation incorporating general prior information improves parameter estimate stability, overall variability in estimates, and power for IFA models with sparse, categorical indicators. Importantly, the priors proposed here can be generally applied to many research contexts in psychology, and they do not impact results compared to ML when indicators are not sparse. I then apply this method to examine the relationship between suicide ideation and insomnia in a sample of first-year college students. This provides an important alternative for researchers who may need to model items with sparse endorsement.

Brain signal variability and executive functions across the life span.

Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.

Traditionally, regional variability in brain signals has been viewed as a source of noise in human neuroimaging research. Our study demonstrates that brain signal variability may contain meaningful information related to psychological processes. We demonstrate that brain signal variability, particularly whole-brain variability, may serve as a reliable indicator of cognitive functions across the life span. Global variability and network-level variability play differing roles in supporting executive functions. Findings suggest that brain signal variability serves as a meaningful indicator of development and cognitive aging.

Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology.

In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.

shinyDLRs: A dashboard to facilitate derivation of diagnostic likelihood ratios.

Despite increased recognition of the importance of evidence-based assessment in clinical psychology, utilization of gold-standard practices remains low, including during diagnostic assessments. One avenue to streamline evidence-based diagnostic assessment is to increase the use of diagnostic likelihood ratios (DLRs), derived from receiver operating characteristic curve analyses. DLRs allow for the adjustment of the likelihood that an individual has a disorder based on self-report data (e.g., questionnaires, psychosocial, family history). Although DLRs provide strong and readily implementable psychometric data to guide diagnostic decision-making, analyses necessary to derive DLRs are not commonplace in psychological curriculum and available resources require familiarity with specialized statistical methodologies and software. We developed a free, researcher-oriented dashboard, shinyDLRs (https://dlrs.shinyapps.io/shinyDLRs/), to facilitate the derivation of DLRs. shinyDLRs allows researchers to carry out multiple analyses while providing descriptive interpretations of statistics derived from receiver operating characteristic curves. We present the utility of this interface as applied to several freely available measures of mood and anxiety for the purposes of guiding diagnosis of psychopathology. The sample leveraged to accomplish this goal included 576 youth, 4-19 years of age, and a parent informant, both of whom completed several questionnaires and semi-structured interviews prior to participating in treatment at a university-based research clinic. Lastly, we provide recommendations for inclusion of DLRs in future research investigating the psychometric properties and diagnostic utility of assessments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Revisiting the factor structure and construct validity of the Cognitive Failures Questionnaire.

The Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982) is an established and commonly used self-report measure of cognitive errors experienced in daily life, capturing perceived difficulties with forgetfulness, distractibility, and thinking blunders. Despite frequent use in clinical research and established associations with psychological and neuropsychological disorders, the psychometric properties and construct validity of the CFQ remain ambiguous. This study sought to critically assess the factor structure and external validity of the CFQ. A sample of 839 people (62% female) between 16 and 85 years of age (M = 44.12, SD = 19.54) was drawn from the Nathan Kline Institute-Rockland Sample. Previously published CFQ factor structures were compared via confirmatory factor analysis and the unique variance explained by each factor was assessed. Next, we related the CFQ latent variables to neuropsychological tasks and symptom measures of depression, anxiety domains, inattention, hyperactivity, and impulsivity. A single-factor model was best supported by the data, indicating that the CFQ represents a global measure of subjective cognitive difficulties rather than errors in specific domains. Scores on the CFQ did not predict poorer performance on objective neuropsychological tasks but were related to a range of psychological distress symptoms. Subscales derived from previously published factor structures may provide misleading impressions of the construct validity of the CFQ and are not recommended for use in future research or clinical contexts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).