scholarly journals Association between negative cognitive bias and depression: A symptom-level approach

2018 ◽  
Author(s):  
Christopher G Beevers ◽  
Michael C Mullarkey ◽  
Justin Dainer-Best ◽  
Rochelle A Stewart ◽  
Jocelyn Labrada ◽  
...  
Keyword(s):  
Cognitive Bias ◽  
Cognitive Biases ◽  
Eye Gaze ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Symptoms Of Depression ◽  
Symptom Level ◽  
General Scientific ◽  
Symptom Predictors ◽  
Variance Explained

Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and it is unlikely that all symptoms are associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identifythe most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory – II. Performance of models was evaluated using predictive R-squared (𝑅2 𝑝𝑟𝑒𝑑), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the Self- Referent Encoding Task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression. General Scientific Summary: This study finds that many symptoms of depression are not strongly associated with thinking negatively about oneself or attending to negative information. This implies that negative cognitive biases may not be strongly associated with depression per se, but may instead contribute to the maintenance of specific depression symptoms, such as sadness, self-dislike, pessimism, feelings of punishment, and indecision.

scholarly journals Symptom centrality and infrequency of endorsement identify adolescent depression symptoms more strongly associated with life satisfaction

2018 ◽  
Author(s):  
Michael C Mullarkey ◽  
Igor Marchetti ◽  
Karen Bluth ◽  
Caryn L Carlson ◽  
Jason Shumake ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Adolescent Depression ◽  
Community Sample ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Cross Sectional ◽  
Fine Grained ◽  
Symptoms Of Depression ◽  
Large N ◽  
Symptom Level

Although depression symptoms are often treated as interchangeable, some symptoms may relate to adolescent life satisfaction more strongly than others. To assess this premise, we first conducted a network analysis on the Mood and Feelings Questionnaire (MFQ) in a large (N = 1,059), cross-sectional sample of community adolescents (age M = 14.72 ± 1.79). The most central symptoms of adolescent depression, as indexed by strength, were self-hatred, loneliness, sadness, and worthlessness while the least frequently endorsed symptoms were self-hatred, anhedonia, feeling like a bad person, and feeling unloved. Moreover, the more central a depression symptom was in the network (i.e., higher strength), the more variance it shared with life satisfaction (r = 0.59, 95% CI: 0.27, 0.76). How frequently a symptom was endorsed was negatively associated with the variance symptoms shared with life satisfaction (r = -0.48, 95% CI: -0.63, -0.21). Cross-validated, prediction focused models found central symptoms were expected to predict more out of fold variance in life satisfaction than peripheral symptoms and frequently endorsed symptoms, but not the least frequently endorsed symptoms. These findings show certain depression symptoms may be more strongly associated with life satisfaction in adolescence and these symptoms can be identified by multiple symptom-level metrics. Limitations include use of cross-sectional data and utilizing a community sample. Better understanding which symptoms of depression share more variance with important outcomes like life satisfaction could help us develop a more fine-grained understanding of adolescent depression.

scholarly journals Deep learning for prediction of depressive symptoms in a large textual dataset

2021 ◽  
Author(s):  
Md Zia Uddin ◽  
Kim Kristoffer Dysthe ◽  
Asbjørn Følstad ◽  
Petter Bae Brandtzaeg
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Depressive Symptoms ◽  
Intelligent Systems ◽  
Superior Performance ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Information Channel ◽  
Symptoms Of Depression ◽  
Prediction Approach

AbstractDepression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

scholarly journals Neurocognitive predictors of self-reported reward responsivity and approach motivation in depression: a data-driven approach

2019 ◽  
Author(s):  
Kean J. Hsu ◽  
Molly McNamara ◽  
Jason Shumake ◽  
Rochelle Ann Stewart ◽  
Jocelyn Labrada ◽  
...  
Keyword(s):  
Symptom Severity ◽  
Cognitive Biases ◽  
Severe Depression ◽  
Test Sample ◽  
Data Driven ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Approach Motivation ◽  
Referential Processing ◽  
Reward Responsivity

Background: Individual differences in reward-related processes, such as reward responsivity and approach motivation, appear to play a role in the nature and course of depression. Prior work suggests that cognitive biases for valenced information may contribute to these reward processes. Yet there is little work examining how biased attention, processing, and memory for positively- and negatively-valenced information may be associated with reward-related processes in samples with depression symptoms. Methods: We used a data-driven, machine-learning (elastic net) approach to identify the best predictors of self-reported reward-related processes using multiple tasks of attention, processing, and memory for valenced information measured across behavioral, eye tracking, psychophysiological, and computational modeling approaches (N = 202). Participants were adults (ages 18 - 35) who ranged in depression symptom severity from mild to severe. Results: Models predicted between 5.0-12.2% and 9.7-28.0% of held-out test sample variance in approach motivation and reward responsivity, respectively. Low self-referential processing of positively-valenced information was the most robust, albeit modest, predictor of low approach motivation and reward responsivity. Conclusions: Self-referential processing of positive information is the strongest predictor of reward responsivity and approach motivation in a sample ranging from mild to severe depression symptom severity. Experiments are now needed to clarify the causal relationship between self-referential processing of positively-valenced information and reward processes in depression.

scholarly journals THE EFFECT OF ACTIVITIES OF DAILY LIVING AND DEPRESSION SYMPTOM LEVEL ON SLEEP QUALITY IN THE ELDERLY WITH HEART FAILURE

2021 ◽  
Vol 54 (1) ◽  
pp. 64-72
Author(s):  
Abdullah Avcı ◽  
Meral GÜN
Keyword(s):  
Heart Failure ◽  
Sleep Quality ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Short Form ◽  
The Elderly ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Symptom Level

Objective: To determine the effect of activities of daily living and depression symptom level on sleep quality in the elderly with heart failure. Methodology: In this descriptive study the sample consisted of 95 patients presented to the cardiology outpatient clinic of a university hospital due to heart failure and who met the inclusion criteria of the study. The research data wss collected using the Personal Information Form, the Pittsburgh Sleep Quality Index, the Katz Index-Activities of Daily Living and the Geriatric Depression Scale-Short Form. Results: It was found that the sleep quality of all patients who participated in the study was low (9.98 ± 2.74). The mean depression symptom level score of the patients was high (7.58 ± 3.58), and that sleep quality decreased as the depression symptom level score increased (p<0.05). There was no relationship between the total activities of daily living score and the total sleep quality score, and that the sleep quality of the dependent patients in the washing and transfer dimensions, which are the sub-dimensions of activities of daily living, were lower than that of the independent ones. Also, it was found that as the level of dependence increased in the daily living activities increased, the level of depression symptoms increased too. Conclusions: The study revealed that elderly patients with heart failure experienced significant sleep problems and that their sleep quality decreased as the depression symptom levels increased.

scholarly journals Cognitive bias modification to prevent depression (COPE): results of a randomised controlled trial

2019 ◽  
Vol 50 (15) ◽  
pp. 2514-2525 ◽  
Author(s):  
Julian Basanovic ◽  
Ben Grafton ◽  
Andrew Ford ◽  
Varsha Hirani ◽  
David Glance ◽  
...  
Keyword(s):  
Cognitive Biases ◽  
Controlled Trial ◽  
Depression Symptoms ◽  
Major Depressive ◽  
Symptoms Of Depression ◽  
Control Intervention ◽  
Severity Of Depression ◽  
Clinically Significant ◽  
Major Depressive Episodes ◽  
Depressive Episodes

AbstractBackgroundAlthough efficacious treatments for major depression are available, efficacy is suboptimal and recurrence is common. Effective preventive strategies could reduce disability associated with the disorder, but current options are limited. Cognitive bias modification (CBM) is a novel and safe intervention that attenuates biases associated with depression. This study investigated whether the delivery of a CBM programme designed to attenuate negative cognitive biases over a period of 1 year would decrease the incidence of major depression among adults with subthreshold symptoms of depression.MethodsRandomised double-blind controlled trial delivered an active CBM intervention or a control intervention over 52 weeks. Two hundred and two community-dwelling adults who reported subthreshold levels of depression were randomised (100 intervention, 102 control). The primary outcome of interest was the incidence of major depressive episode assessed at 11, 27 and 52 weeks. Secondary outcomes included onset of clinically significant symptoms of depression, change in severity of depression symptoms and change in cognitive biases.ResultsAdherence to the interventions was modest though did not differ between conditions. Incidence of major depressive episodes was low. Conditions did not differ in the incidence of major depressive episodes. Likewise, conditions did not differ in the incidence of clinically significant levels of depression, change in the severity of depression symptoms or change in cognitive biases.ConclusionsActive CBM intervention did not decrease the incidence of major depressive episodes as compared to a control intervention. However, adherence to the intervention programme was modest and the programme failed to modify the expected mechanism of action.

scholarly journals Mental Health Status of Healthcare Workers During the COVID-19 Outbreak

2021 ◽  
Author(s):  
Christiana Nicolaou ◽  
Joanna Menikou ◽  
Demetris Lamnisos ◽  
Jelena Lubenko ◽  
Giovambattista Presti ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Perceived Stress ◽  
Healthcare Workers ◽  
Perceived Social Support ◽  
Mental Healthcare ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Health Crisis ◽  
Symptoms Of Depression

Abstract. Background: The COVID-19 pandemic is a massive health crisis that has exerted enormous physical and psychological pressure. Mental healthcare for healthcare workers (HCWs) should receive serious consideration. This study served to determine the mental-health outcomes of 1,556 HCWs from 45 countries who participated in the COVID-19 IMPACT project, and to examine the predictors of the outcomes during the first pandemic wave. Methods: Outcomes assessed were self-reported perceived stress, depression symptom, and sleep changes. The predictors examined included sociodemographic factors and perceived social support. Results: The results demonstrated that half of the HCWs had moderate levels of perceived stress and symptoms of depression. Half of the HCWs ( n = 800, 51.4%) had similar sleeping patterns since the pandemic started, and one in four slept more or slept less. HCWs reported less perceived stress and depression symptoms and higher levels of perceived social support than the general population who participated in the same project. Predictors associated with higher perceived stress and symptoms of depression among HCWs included female sex, not having children, living with parents, lower educational level, and lower social support. Discussion: The need for establishing ways to mitigate mental-health risks and adjusting psychological interventions and support for HCWs seems to be significant as the pandemic continues.

scholarly journals What is the Role of Internalizing and Externalizing Symptoms in Adolescent Suicide Behaviors?

2019 ◽  
Vol 16 (14) ◽  
pp. 2511 ◽  
Author(s):  
José Antonio Piqueras ◽  
Victoria Soto-Sanz ◽  
Jesús Rodríguez-Marín ◽  
Carlos García-Oliva
Keyword(s):  
Adolescent Suicide ◽  
Depression Symptoms ◽  
Suicidal Behaviors ◽  
Symptoms Of Depression ◽  
Increased Risk ◽  
Suicide Behaviors ◽  
Internalizing And Externalizing ◽  
Variance Explained ◽  
Internalizing And Externalizing Symptoms

Suicide is the second leading cause of death in adolescents and young adults aged 15 to 29 years. Specifically, the presence of internalizing and externalizing symptomatology is related to increased risk for suicide at these ages. Few studies have analyzed the relations between these symptoms and their role as mediators in predicting suicide behavior. This study aimed to examine the relation between internalizing and externalizing symptomatology and suicide behaviors through a longitudinal study. The sample consisted of 238 adolescents aged 12 to 18 years. The data were analyzed via the PROCESS Statistical Package. The main results showed that previous depression symptoms had a significant indirect effect, through previous suicide behaviors and current depression symptoms, on current suicide behaviors, accounting for 61% of the total variance explained. Additionally, being a girl increased this risk. Therefore, the implementation of early identification and intervention programs to address youth symptoms of depression and suicidal behaviors could significantly reduce the risk for future suicidal behaviors in adolescence.

scholarly journals Network analyses reveal which symptoms improve (or not) following an Internet intervention (Deprexis) for depression

2019 ◽  
Author(s):  
Michael C Mullarkey ◽  
Aliza Stein ◽  
Rahel Pearson ◽  
Christopher G Beevers
Keyword(s):  
The United States ◽  
Internet Intervention ◽  
Depression Symptoms ◽  
Network Analyses ◽  
Symptoms Of Depression ◽  
Symptom Level ◽  
Nuanced Understanding ◽  
Sum Scores ◽  
Meta Analyses ◽  
Specific Symptoms

Background: Depression is a heterogeneous collection of symptoms. Prior meta-analyses using symptom sum scores have shown the Internet intervention, Deprexis, to be an efficacious treatment for depression. However, no prior research has investigated how Deprexis (or any other Internet intervention for depression) impacts specific symptoms of depression. The current study utilizes symptom-level analyses to examine which symptoms are directly, indirectly, or minimally influenced by treatment. Methods: Network analysis and mean-level approaches examined which symptoms, assessed by the Quick Inventory of Depression Symptoms (QIDS-SR), were affected by an 8-week course of Deprexis compared to a waitlist in a nationally recruited sample from the United States (N = 295). Results: Deprexis directly improved the symptoms of sadness and indecision. Change in these symptoms, in turn, were associated with change in early insomnia, middle insomnia, self-dislike, fatigue, anhedonia, suicidality, slowness, and agitation. All of these symptoms (except for agitation and early insomnia) show decreases with Deprexis compared to a waitlist after correcting for multiple comparisons. Six additional symptoms, particularly the somatic symptoms, were not impacted by Deprexis compared to waitlist. Conclusions: In this sample, the efficacy of Deprexis was due to its direct impact on sadness and indecision. Examining treatment-related change in specific symptoms may facilitate a more nuanced understanding of how a treatment works compared to examining symptom sum scores. Symptom-level approaches may also identify symptoms that do not improve and provide important direction for future treatment development.

What is the Role of Internalizing and Externalizing Symptoms in Adolescent Suicide Behaviors?

2019 ◽  
Author(s):  
José A. Piqueras ◽  
Victoria Soto-Sanz ◽  
Jesús Rodríguez-Marín ◽  
Carlos García-Oliva
Keyword(s):  
Adolescent Suicide ◽  
Depression Symptoms ◽  
Suicidal Behaviors ◽  
Symptoms Of Depression ◽  
Increased Risk ◽  
Suicide Behaviors ◽  
Internalizing And Externalizing ◽  
Variance Explained ◽  
Internalizing And Externalizing Symptoms

Suicide is the second leading cause of death in adolescents and young adults aged 15 to 29 years. Specifically, the presence of internalizing and externalizing symptomatology is related to increased risk for suicide at these ages. Few studies have analyzed the relations between these symptoms and their role as mediators in predicting suicide behavior. This study aimed to examine the relation between internalizing and externalizing symptomatology and suicide behaviors through a longitudinal study. The sample consisted of 238 adolescents aged 12 to 18 years. The data were analyzed via PROCESS Statistical Package. The main results showed that previous depression symptoms had a significant indirect effect, through previous suicide behaviors and current depression symptoms, on current suicide behaviors, accounting for 61% of the total variance explained. Additionally, being a girl increased this risk. Therefore, the implementation of early identification and intervention programs to address youth symptoms of depression and suicidal behaviors could significantly reduce the risk for future suicidal behaviors in adolescence.

Bereavement or Breakup: A Network Approach to Modeling Differential Pathways into Depression

2019 ◽  
Author(s):  
Julian Burger ◽  
Margaret S. Stroebe ◽  
Pasqualina Perrig-Chiello ◽  
Henk A.W. Schut ◽  
Stefanie Spahni ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Graphical Models ◽  
Contextual Factors ◽  
Marital Disruption ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Multiple Time ◽  
Cross Sectional ◽  
Network Analyses ◽  
Within Subjects

Background: Prior network analyses demonstrated that the death of a loved one potentially precedes specific depression symptoms, primarily loneliness, which in turn links to other depressive symptoms. In this study, we extend prior research by comparing depression symptom network structures following two types of marital disruption: bereavement versus separation. Methods: We fitted two Gaussian Graphical Models to cross-sectional data from a Swiss survey of older persons (145 bereaved, 217 separated, and 362 married controls), and compared symptom levels across bereaved and separated individuals. Results: Separated compared to widowed individuals were more likely to perceive an unfriendly environment and oneself as a failure. Both types of marital disruption were linked primarily to loneliness, from where different relations emerged to other depressive symptoms. Amongst others, loneliness had a stronger connection to perceiving oneself as a failure in separated compared to widowed individuals. Conversely, loneliness had a stronger connection to getting going in widowed individuals. Limitations: Analyses are based on cross-sectional between-subjects data, and conclusions regarding dynamic processes on the within-subjects level remain putative. Further, some of the estimated parameters in the network exhibited overlapping confidence intervals and their order needs to be interpreted with care. Replications should thus aim for studies with multiple time points and larger samples. Conclusions: The findings of this study add to a growing body of literature indicating that depressive symptom patterns depend on contextual factors. If replicated on the within-subjects level, such findings have implications for setting up patient-tailored treatment approaches in dependence of contextual factors.

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