Predictors Of Depressive Symptoms Research Articles

Ventral striatal-cingulate resting-state functional connectivity in healthy adolescents relates to later depression symptoms in adulthood

BackgroundDepression is a significant public health concern. Identifying biopsychosocial risk factors for depression is important for developing targeted prevention. Studies have demonstrated that blunted striatal activation during reward processing is a risk factor for depression; however, few have prospectively examined whether adolescent reward-related resting-state functional connectivity (rsFC) predicts depression symptoms in adulthood and how this relates to known risk factors (e.g., childhood trauma). MethodsAt baseline, 66 adolescents (mean age = 14.7, SD = 1.4, 68 % female) underwent rsFC magnetic resonance imaging and completed the Children's Depression Inventory (CDI). At follow-up (mean time between adolescent scan and adult follow-up = 10.1 years, SD = 1.6, mean adult age = 24.8 years, SD = 1.7), participants completed the Childhood Trauma Questionnaire (CTQ) and Beck Depression Inventory– Second Edition (BDI-2). Average rsFC was calculated between nodes in mesocorticolimbic reward circuitry: ventral striatum (VS), rostral anterior cingulate cortex (rACC), medial orbitofrontal cortex, and ventral tegmental area. Linear regressions assessed associations between rsFC, BDI-2, and CTQ, controlling for adolescent CDI, sex assigned at birth, and scan age (Bonferroni corrected). ResultsGreater childhood trauma was associated with higher adulthood depression symptoms. Stronger VS-rACC rsFC during adolescence was associated with greater depression symptoms in adulthood and greater childhood trauma. LimitationsThe small sample size, limited depression severity, and seed-based approach are limitations. ConclusionsThe associations between adolescent striatal-cingulate rsFC and childhood trauma and adult depression symptoms suggest this connectivity may be an early neurobiological risk factor for depression and that early life experience plays an important role. Increased VS-rACC connectivity may represent an over-regulatory response on the striatum, commonly reported in depression, and warrants further investigation.

Anxiety Symptom Trajectories Predict Depression Symptom Trajectories up to Four Years After CBT for Youth Anxiety Disorders.

Long-term data on depression symptoms after cognitive behavioral therapy (CBT) for youth anxiety disorders are scant. We examined depression symptoms up to four years post CBT for anxiety addressing youth age and gender, family social class, and parent mental health as predictors. The sample comprised 179 youth (M age at pre-treatment = 11.5 years; SD = 2.1) in a randomized controlled trial. Clinically assessed anxiety diagnoses and youth and parent-reported anxiety and depression symptoms were measured before, after, and one and four years after CBT. Parent self-reported mental health was measured before CBT. We used regression analyses to determine whether full diagnostic recovery at post-CBT predicted depression trajectories across the four-year assessment period. We used growth curve models to determine whether anxiety trajectories predicted depression trajectories across the four-year assessment period. Youth who lost their anxiety diagnoses after CBT had significantly lower parent-reported depression levels over time, but not lower youth self-reported depression levels. The anxiety symptom trajectory predicted the depression symptom trajectory up to four years post-treatment. There was more explained variance for within-informant (youth-youth; parent-parent) than cross-informants. Being older, female, having lower socio-economic status and parents with poorer mental health were associated with more youth-rated depression over time. However, these demographic predictors were not significant when anxiety symptoms trajectories were added to the models. Successful CBT for anxiety in children is associated with less depression symptoms for as long as four years. Anxiety symptom improvement appears to be a stronger predictor that demographic variables and parent mental health.

Two-year retest reliability and predictive validity of the Self- and Informant-Personality Inventory for ICD-11 in older adults.

The International Classification of Diseases, 11th edition (ICD-11) adopted a fully dimensional model of personality disorder. The Personality Inventory for ICD-11 (PiCD) and Informant-Personality Inventory for ICD-11 (IPiC) were developed to assess the ICD-11 trait model, and the PiCD has since received significant validation support. However, there has only been one prior study of longitudinal predictive validity of the PiCD, two relatively short test-retest reliability studies of the PiCD, and no prior longitudinal tests of the IPiC. Longitudinal psychometric support for psychological assessment measures is essential. The present study provides a longer, larger, 2-year psychometric validation test of the PiCD and IPiC. Participants (N = 711) and their informants (N = 569) were recruited in the St. Louis Personality and Aging Network. The results demonstrated strong 2-year retest reliability for the PiCD and IPiC, as well as mean-level stability. Additionally, we explored the relationships between the PiCD and IPiC and important life outcome measures (depressive symptoms, satisfaction with life, and health status). The analysis revealed several significant associations between PiCD and IPiC scales and the outcome variables across time. Further, the PiCD Negative Affectivity and IPiC Detachment scales demonstrated incremental validity over each other and the outcome variables at Wave 1 in the prediction of depressive symptoms and satisfaction with life, respectively. The findings provide essential longitudinal test-retest reliability and predictive validity support for the PiCD and IPiC. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of depressive symptoms at high age (80+) by psychological, biological and functional factors

BackgroundLate-life depression (LLD) is highly prevalent, especially in people aged 80 years and older. We aimed to investigate predictors and their influence on depressive symptoms in LLD. MethodsWe analysed data from the NRW80+ study, a population-based cross-sectional study of individuals aged 80 years and older. Data from n = 926 cognitively unimpaired participants were included. We reduced 95 variables to 21 predictors of depressive symptoms by using a two-step cluster analysis (TSCA), which were assigned to one of four factors (function, values and lifestyle, autonomy and contentment, biological-somatic) according to a principal component analysis. A second TSCA with complete data sets (n = 879) was used to define clusters of participants. Using weighted mean composite scores (CS) for each factor group, binary logistic regression analyses were performed to predict depressive symptoms for each cluster and the total population. ResultsThe second TSCA yielded two clusters (cluster 1 (n = 688), cluster 2 (n = 191)). The proportion of participants with depressive symptoms was significantly higher in cluster 2 compared to cluster 1 (39 % vs. 15 %; OR = 3.6; 95 % CI 2.5–5.1; p < .001). Participants in cluster 2 were significantly older (mean age 88 vs. 85 years; p < .001), with a higher proportion of women (56 % vs. 46 %; OR = 1.5; 95 % CI 1.1–2.0; p = .016), had a higher BMI (p = .017), lower financial resources (OR = 2.3; 95 % CI 1.6–3.5; p < .001), lower educational level (OR = 1.8; 95 % CI 1.2–2.5; p = .002), higher proportion of single, separated or widowed participants (OR = 1.9; 95 % CI 1.3–2.6; p < .001) and a smaller mean social network (p = .044) compared to cluster 1. Binary logistic regression analyses showed that the weighted mean CS including the autonomy and contentment predictors explained the largest proportion of variance (22.8 %) for depressive symptoms in the total population (Nagelkerke's R2 = 0.228, p < .001) and in both clusters (cluster 1: Nagelkerke's R2 = 0.171, p < .001; cluster 2: Nagelkerke's R2 = 0.213, p < .001), respectively. LimitationsThe main limitations are the restriction to cognitively unimpaired individuals and the use of a self-rated questionnaire to assess depressive symptoms. ConclusionsPsychological factors such as autonomy and contentment are critical for the occurrence of depressive symptoms at higher age, independent of the functional and somatic status and may serve as specific targets for psychotherapy.

The Effects of Family Function, Nurse-Patient Communication, and Stigma on Depression Symptoms in Tuberculosis Patients at the DOTS Pulmonary Outpatient Clinic of RSUD Banyumas

Background: Tuberculosis (TB) and mental illness are prevalent worldwide and often coexist. Poor mental health has been linked to alterations in immune function. Through social, behavioral, and biological mechanisms, depression and tuberculosis together amplify the burden of disease. Collaboration from all parties is needed to alleviate depression in TB patients, including support from family function and positive societal stigma. Method: This was a descriptive correlational study, and data collection was carried out using a cross-sectional method. A total of 72 respondents who met the inclusion and exclusion criteria were selected as the sample using a non-probability sampling technique, specifically accidental sampling. Results: The results of the multiple regression test showed that only the variables of family function and stigma had a significant correlation with p-values of 0.00005 and 0.009 0.05, respectively. Meanwhile, the variable of nurse-patient communication had a p-value of 0.633 0.05, indicating no significant correlation. Additionally, the calculated F-value was 18.662, and the tabled F-value was 3.136, with a probability value of 0.000. Since the probability value was less than 0.05, the regression model could be used to predict depression symptoms. It can be stated that the three independent variables, namely family function, nurse-patient communication, and stigma, collectively influence depression symptoms in patients. Conclusion: There is a significant relationship between family function and stigma towards depression symptoms in Tuberculosis patients at the DOTS Pulmonary Clinic of Banyumas Regional Public Hospital in 2023.

Investigating the Effects of Transdiagnostic Processes on Anxiety and Depression Symptoms in Autistic Young People: the Mediating Role of Emotion Dysregulation.

Internalising symptoms are elevated in autism compared to the general population. Few studies have investigated emotional dysregulation (ED) as a potential mediator between specific transdiagnostic processes and anxiety and depression symptoms in autistic youth. In a sample of 94 autistic young people aged 5-18years referred to a specialist clinic for an autism evaluation, we tested the effects of ED as a mediator between cognitive inflexibility (CI), intolerance of uncertainty (IU) and alexithymia with anxiety and depression symptoms, using structural equation modelling. Effect sizes were compared to a non-autistic comparison group (n = 84). CI and alexithymia did not significantly predict depression symptoms in autistic young people, directly nor via ED. Relationships between CI/alexithymia and depression were fully mediated by ED in the non-autistic sample. There was a direct effect of CI on anxiety in the non-autistic group but not in those with a diagnosis. IU predicted depression symptoms in the autism group; and ED mediated this relationship only in those who did not receive a diagnosis. IU directly predicted anxiety in both groups and this relationship did not occur via ED. The finding of a direct pathway from IU to anxiety and depression in autistic youth is consistent with the literature. The finding that CI did not predict anxiety or depression in those with autism is novel, as was the finding that ED mediated relationships between alexithymia and anxiety/depression symptoms in both samples. The findings may have important implications for the delivery of psychological interventions for autistic youth.

The impact of interpersonal traumas in Portuguese women’s psychological wellbeing: predictors of depression and post traumatic stress symptoms

ABSTRACT The impact of child abuse history and adult rape on 302 Portuguese women’s emotional wellbeing was examined as well as the role of emotional regulation and social support in women’s resiliency. A cross-sectional study design was used with retrospective self-report of interpersonal traumas. Women who reported a history of interpersonal trauma also reported more depression and post-traumatic stress (PTSD) symptoms, and more difficulties in emotional regulation than those who reported no abuse history. Hierarchical linear regression showed that reporting a history of child abuse and lower perceived social support predicted depression symptoms. Having lower yearly income, reporting a history of child abuse, cumulative trauma, and lower perceived social support predicted PTSD symptoms. Social support and specific emotional regulation strategies such as cognitive reappraisal may be important constructs to be included in intervention programmes to promote resilience, decrease depression and PTSD symptoms and to buffer the cumulative impact of interpersonal traumas. PRACTICE IMPACT STATEMENT Our results underline the need for long-term support, screening, and evidence-based follow-up care among women who were abused during childhood and that have cumulative trauma instances. A second implication addresses the role of perceived social support as a resiliency factor. Social support interventions that are effective at reducing psychological distress should be implemented in women who have been exposed to abuse. We can also speculate that addressing emotion regulation difficulties (e.g. teaching distress tolerance, awareness of emotions, emotional labelling, acceptance of strong emotions, strategies to manage strong emotions, among others) may be important for treatment purposes.

Exploring Subfactors of Adult Cognitive Disengagement Syndrome and Impact on Neuropsychological Performance

Objective: This study investigated subfactors of cognitive disengagement syndrome (CDS; previously referred as sluggish cognitive tempo) among adults referred for neuropsychological evaluation of attentiondeficit/hyperactivity disorder (ADHD). Method: Retrospective analyses of data from 164 outpatient neuropsychological evaluations examined associations between CDS subfactors and self-reported psychological symptoms and cognitive performance. Results: Factor analysis produced two distinct but positively correlated constructs: “Cognitive Complaints’’ and “Lethargy.” Both correlated positively with symptom reports (rs = 0.26–0.57). Cognitive Complaints correlated negatively with working memory, processing speed, and executive functioning performance (rs = −0.21 to −0.37), whereas Lethargy correlated negatively only with processing speed and executive functioning performance (rs = −0.26 to −0.42). Both predicted depression symptoms, but only Cognitive Complaints predicted inattention symptoms. Both subfactors demonstrated modest to nonsignificant associations with cognitive performance after accounting for estimated premorbid intelligence and inattention. Conclusion: Findings indicate a bidimensional conceptualization of CDS, with differential associations between its constituent subfactors, reported symptoms, and cognitive performance.

A - 120 Fatigue Moderates the Relationship between Negative Attributional Style and Depression Symptoms in Multiple Sclerosis.

To investigate whether fatigue moderates the relationship between negative attributional style and depression symptoms in persons with Multiple Sclerosis (PwMS). Forty-seven PwMS (39 Female) completed a comprehensive neuropsychological test battery and psychosocial questionnaires. The Attributional Style Questionnaire was utilized to create internal, stable, and global attribution dimension scores, and an overall attributional style score combining the three dimensions. Overall, physical, psychosocial, and cognitive fatigue were measured using the Fatigue Impact Scale (FIS). The Beck Depression Inventory-Fast Screen was used to measure depression symptoms. Separate regressions with depression symptoms as the outcome were conducted with attributional style, each type of fatigue, and their interactions included as predictors. Regression analyses revealed that the interaction between attributional style and overall fatigue significantly predicted depression symptoms (p = 0.049). Simple effects tests showed that overall attributional style significantly predicted depression symptoms when overall fatigue was high (p = 0.008). The interactions between attributional style and both physical fatigue (p = 0.003) and psychosocial fatigue (p = 0.039) also significantly predicted depression symptoms. Simple effects tests showed that attributional style was associated with depression symptoms only in PwMS with high physical fatigue (p < 0.001) and high psychosocial fatigue (p = 0.018). Fatigue moderated the relationship between overall attributional style and depression symptoms in PwMS. Specifically, attributional style was only significantly associated with depression symptoms in PwMS reporting high levels of overall, physical, and psychosocial fatigue. Interventions aimed at targeting fatigue may reduce the risk that negative attributional style will lead to depressive symptoms in MS.

Blunted neural response to errors prospectively predicts increased symptoms of depression during the COVID-19 pandemic.

Symptoms of depression have increased during the COVID-19 pandemic, possibly due to increases in both chronic and episodic stress exposure. Yet these increases are being driven by a subset of people, leading to questions of what factors make some people more vulnerable. Individual differences in neural response to errors may confer vulnerability to stress-related psychopathology. However, it is unclear whether neural response to errors prospectively predicts depressive symptoms within the context of chronic and episodic stress exposure. Prior to the pandemic, neural response to errors, measured by the error-related negativity (ERN), and depression symptoms were collected from 105 young adults. Beginning in March 2020 and ending in August 2020, we collected symptoms of depression and exposure to pandemic-related episodic stressors at eight time points. Using multilevel models, we tested whether the ERN predicted depression symptoms across the first 6 months of the pandemic, a period of chronic stress. We also examined whether pandemic-related episodic stressors moderated the association between the ERN and depression symptoms. A blunted ERN predicted increased depression symptoms across the early part of the pandemic, even after adjusting for baseline depression symptoms. Moreover, episodic stress interacted with the ERN to predict concurrent symptoms of depression: For individuals exposed to greater episodic stress, a blunted ERN was associated with increased depressive symptoms at each timepoint during the pandemic. These findings indicate that blunted neural response to errors may enhance risk for depression symptoms under conditions of real-world chronic and episodic stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

E-Cigarette Dependence and Depressive Symptoms Among Youth

Although the relationship between smoking and depression has been well-established, little is known about the association between use of e-cigarette and depression, particularly among youth and young adults. This study proposes that e-cigarette dependence, rather than simply use, serves as a potential stressor and may interact with pre-existing vulnerabilities to contribute to depression in youth, consistent with the diathesis-stress theory. This study examines the longitudinal association of vaping dependence and vaping frequency on depression symptoms among youth and young adults who have never smoked cigarettes. People who used e-cigarettes in the past month who reported never smoking a cigarette (N=1,226) aged between 16 and 25 years were followed longitudinally every 3 months for up to 1 year beginning in 2020. The Penn State E-Cigarette Dependence Index at time t was used to predict depression symptoms assessed using the Center for Epidemiologic Studies Depression Scale at time t+1. A total of 32.1% reported vaping in the past month with the Penn State E-Cigarette Dependence Index score (M=8.5) and a Center for Epidemiologic Studies Depression Scale score (M=15.8). Higher vaping dependence scores were significantly associated with increased depression symptoms scores at follow-up among youth and adults (β=0.08; 95% CI=0.01, 0.15), controlling for baseline depression symptom scores and covariates. Although vaping dependence was highly associated with vaping frequency level, no significant association between the frequency of vaping and depression was found (β= -0.33; 95% CI= 1.21, 0.54). These results are consistent with the diathesis-stress model of the relationship between substance use and depression. Vaping dependence but not vaping frequency was associated with increased depressive symptoms among people who never smoked cigarettes.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.

Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. We aimed to address these 3 challenges to inform future work in stratified analyses. Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model.

Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study. Cross-sectional. Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

Change in Finances, Peer Access, and Mental Health Among Trans and Nonbinary People During the COVID-19 Pandemic.

Purpose: Due to structural transphobia, trans and nonbinary (TNB) individuals were particularly vulnerable to the negative effects of social isolation and financial instability resulting from COVID-19. The present study examined the effect of change in finances and access to TNB peer gatherings on anxiety and depression during the COVID-19 pandemic. Methods: Participants were 18 years and older (mean = 30) and completed prepandemic baseline (Fall 2019) and pandemic follow-up (Fall 2020) surveys. Multivariable regressions examined associations between mental health and change in (1) finances and (2) access to TNB peer gatherings (in person or online). Results: Of 780 participants, 50% reported that the COVID-19 pandemic had a negative impact on personal income and 58.3% reported negative impact on access to TNB peer gatherings. Depression and anxiety symptoms increased from prepandemic to follow-up, and most participants were above measurement cutoffs for clinical levels at both time points. Change in finances and access to TNB peer gatherings interacted with prepandemic depression scores to predict depression symptoms during the COVID-19 pandemic. For participants with high prepandemic depression scores, financial stability predicted pandemic depression scores comparable to that predicted by negative financial change. No interaction was found between these variables when predicting anxiety symptoms during the COVID-19 pandemic. Conclusion: Findings underscore the influence of inequality and prepandemic mental health when considering the impact of COVID-19 on wellbeing. Results suggest need for multifaceted programs and services, including financial support and meaningful TNB community engagement, to address barriers to health equity posed by systematic gender oppression.

The Relationship between Minority Stress and Depressive Symptoms in the LGBTQA Population from Poland.

The cross-sectional study examines minority stress and depression symptoms regarding various sexual and gender minority (SGM) identities in lesbian, gay, bisexual, transgender, queer, and asexual (LGBTQA) individuals from Poland. The online survey was conducted among 509 people. Participants aged between 18 and 47 (M = 22.39, SD = 4.78). Gender identity included 262 cisgender women, 74 cisgender men, 31 transgender women, 53 transgender men, and 89 nonbinary individuals. Sexual identity comprises 197 bisexual, 150 homosexual, 78 pansexual, 33 asexual, 21 undefined, 14 heterosexual, 9 demisexuals, 6 queer, and 1 sapiosexual individual. The Daily Heterosexist Experiences Questionnaire (DHEQ) and the Center for Epidemiologic Studies Depression Scale-Revised (CESD-R) were used to measure minority stress and depression symptoms, respectively. Among LGBTQA participants, 99.80% declared minority stress at least once during the past year. In particular, vicarious trauma was experienced in 99.80% of participants, vigilance in 95.87%, harassment and discrimination in 80.35%, stress related to the family of origin in 69.16%, and to gender expression in 68.76% of respondents. Depression symptoms were found in 62.50% of respondents. Significantly higher rates of depression and minority stress were presented in dual than single SGM individuals. Binomial logistic regression showed that such sources of minority stress as vigilance, harassment, and gender expression could predict depression symptoms. Therefore, prevention and intervention programs should be designed for the LGBTQA population focusing on coping with these sources of minority stress, especially among those of dual SGM identity.

Development and performance analysis of machine learning methods for predicting depression among menopausal women

Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall​ Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.

Overgeneralization as a Predictor of the Course of Depression Over Time: The Role of Negative Overgeneralization to the Self, Negative Overgeneralization Across Situations, and Overgeneral Autobiographical Memory

BackgroundDepression is characterized by different forms of overgeneralization that are all assumed to play a causal role in the development and course of depression.MethodsWe examined, in a community sample of over 625 individuals, whether these different forms of overgeneralization are correlated and whether they are prospective predictors of depression at 6-month follow-up.ResultsNegative overgeneralization to the self and across situations—two types of overgeneralized thinking processes—were significantly but weakly related, but neither of them was related to overgeneral memory—a memory-based form of overgeneralization. Overgeneralization to the self and overgeneral memory both predicted depression symptoms at follow-up. Further, two and three-way interactions indicated that higher levels of overgeneralization processes interact to predict depressive symptoms. Overgeneralization to the self and overgeneral memory both independently predicted probable recurrence of a major depressive episode during the follow-up period in individuals that formerly experienced depression.ConclusionsFindings suggest that overgeneralization in depression is not a unitary construct and that different overgeneralization processes play independent and interacting roles in the course of depression.

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.

The Relationship between Anxiety, Subjective and Objective Sleep, Chronotype and Circadian Rhythms with Depressive Symptoms in Insomnia Disorder.

Insomnia is a highly prevalent sleep disorder with strong bidirectional associations with depressive symptoms. The circadian preference for eveningness has been shown to be associated with depressive symptoms in insomnia and other mental health conditions. However, there is a lack of studies in insomnia investigating whether objective measures, such as dim light melatonin onset (DLMO) or polysomnographic (PSG) sleep, are associated with depressive symptoms. Therefore, we investigated the associations between subjective measures (questionnaires assessing anxiety, sleep quality and circadian preference, and sleep diary) and depressive symptoms and whether the addition of objective measures (DLMO, PSG parameters) would strengthen the associations with depressive symptoms. In 115 insomnia disorder patients we found that anxiety was strongly associated with depressive symptoms in a model including circadian preference, dysfunctional beliefs of sleep, and self-reported previous depressive symptoms (R2 = 0.496, p < 0.001). The addition of sleep diary measures did not strengthen the model. We also found that the addition of objective measures (DLMO, PSG parameters) did not improve the subjective associations with depressive symptoms. Our data suggest that objective circadian markers are less important in the prediction of depressive symptoms in insomnia compared to subjective measures.

Anxiety and Depression Among College Students During the COVID-19 Lockdown: A Cross-Sectional Survey in Jiangsu, China.

This study aimed to explore the levels of anxiety and depression in a sample of home-quarantined college students to identify the risk factors for psychological distress during the COVID-19 lockdown. A total of 1156 college students studying in Jiangsu, China, participated from August 5 to August 14. A structured questionnaire was used to collect data anonymously, including demographic characteristics, the generalized anxiety disorder-7 (GAD-7) questionnaire, the patient health questionnaire-9 (PHQ-9), a physical activity scale, and items related to COVID-19. The chi-square test and Fisher's exact test were employed to establish the differences in levels of anxiety and depression across sociodemographic characteristics. Binary logistic regression was applied to measure the predictors of levels of anxiety and depression, and associations were considered significant at p <0.05. The estimates of anxiety and depression were 48.1% and 57.6%, respectively. The univariate analysis indicated a significant difference in anxiety levels across student grades, whether the student was an only child, the distance from the worst-hit areas, and physical activity/exercise intensity. Physical activity intensity and living in communities with infected people were statistically correlated with the level of depression. Binary logistic regression results indicated that the predictive factors for anxiety were living within a short distance from the worst-hit areas (10~20 km), engaging in higher education (graduate students), and low-intensity daily exercise. Factors statistically predicting depression symptoms were having siblings, a COVID-19 diagnosis in the community and low-intensity daily exercise. During outbreaks, students in an extremely stressful state are more likely to develop anxiety and depression, particularly postgraduates. Psychological interventions to reduce fears and encourage exercise should be available to home-quarantined college students. Students who live in the worst-hit areas and are not the only child in the family should be prioritized.