Depression symptoms in late life assessed using the EURO–D scale

1999 ◽  
Vol 174 (4) ◽  
pp. 339-345 ◽  
Author(s):  
M. J. Prince ◽  
A. T. F. Beekman ◽  
D. J. H. Deeg ◽  
R. Fuhrer ◽  
S.-L. Kivela ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Gender Difference ◽  
Cognitive Decline ◽  
Marital Status ◽  
Older Persons ◽  
Positive Association ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Symptom Scale ◽  
Two Factors

BackgroundData from surveys involving 21 724 subjects aged ⩾65 years were analysed using a harmonised depression symptom scale, the EURO–D.AimsTo describe and compare the effects of age, gender and mental status on depressive symptoms across Europe.MethodWe tested for the effects of centre, age, gender and marital status on EURO–D score. Between-centre variance was partitioned according to centre characteristics: region, religion and survey instrument used.ResultsEURO–D scores tended to increase with age, women scored higher than men, and widowed and separated subjects scored higher than others. The EURO–D scale could be reduced into two factors: affective suffering, responsible for the gender difference, and motivation, accounting for the positive association with age.ConclusionsLarge between-centre differences in depression symptoms were not explained by demography or by the depression measure used in the survey. Consistent, small effects of age, gender and marital status were observed across Europe. Depression may be overdiagnosed in older persons because of an increase in lack of motivation that may be affectively neutral, and is possibly related to cognitive decline.

scholarly journals THE RELATIONSHIP BETWEEN MARITAL STATUS, COHORT, AND DEPRESSION SYMPTOMS IN TAIWAN

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S311-S312
Author(s):  
Fang-Yi Huang ◽  
Min Li
Keyword(s):  
Depressive Symptoms ◽  
Marital Status ◽  
Baby Boom ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Economic Decline ◽  
Logistic Regression Models ◽  
Financial Decline ◽  
Married Female ◽  
The Relationship

Abstract Objectives: The relationship between marital status and depression symptoms is well documented. However, how the negative economic shock affect relationship differ by gender and cohort is still indecisive. The dataset “2011 wave of the Taiwan Longitudinal Study in Aging” and logistic regression models were used in the study. The results: Marital status is related to depression symptoms, but it differs by gendered cohort. With considering financial shock, there is no difference of depressive symptom between divorced and married female. The divorced and widowed have 4.81 and 2.47 times higher of getting depression symptom than the married for baby boom female. Being divorced is 3.67 times higher of getting depressive symptoms than being married for baby boom male. For WWII female, the widows are 1.78 times higher to have depressive symptoms than the married. being divorced, widowers, and single are 3.32, 2.21 and 2.90 times higher of getting depressive symptoms than being married for WWII male. Being divorced is 3.67 times higher of getting depressive symptoms than being married for baby boom male. In conclusions, people with unstable marital statuses are more depressed than the married. In particular, the effect of unstable marital statuses on depression could be account for by financial decline for women but not men. Given the policy emphasis on those with unstable marital status and economic decline, divorce female and single baby boom female may represent particular groups in whom interventions designed to financially support.

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.

scholarly journals FKBP5 Genotype Linked to Combined PTSD-Depression Symptom in Chinese Earthquake Survivors

2019 ◽  
Vol 64 (12) ◽  
pp. 863-871
Author(s):  
Gen Li ◽  
Li Wang ◽  
Kunlin Zhang ◽  
Chengqi Cao ◽  
Xing Cao ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Genetic Basis ◽  
Latent Profile Analysis ◽  
Common Mental Disorders ◽  
Phenotypic Heterogeneity ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Environment Interaction ◽  
Post Traumatic Stress ◽  
2008 Wenchuan Earthquake

Background: Post-traumatic stress disorder (PTSD) and depression are common mental disorders in individuals experiencing traumatic events. To date, few studies have studied the relationship between genetic basis and phenotypic heterogeneity of traumatized individuals. The present study examined the effects of four FKBP5 SNPs (rs1360780, rs3800373, rs9296158, and rs9470080) in four postdisaster groups (low symptom, predominantly depressive, predominantly PTSD, and combined PTSD-depression symptom groups) as identified by latent profile analysis. Methods: A total of 1,140 adults who experienced the 2008 Wenchuan earthquake participated in our study. Earthquake-related trauma, PTSD, and depressive symptoms were measured using standard psychometric instruments. The four FKBP5 SNPs were genotyped using a custom-by-design 2 × 48-Plex SNP scan™ Kit. Results: After adjusting for covariates, the main and gene–environment interaction effects of rs9470080 were all significant when the combined PTSD-depression group was compared with the low symptoms, predominantly depression and predominantly PTSD groups. rs9470080 TT genotype carriers had a higher risk of developing high co-occurring PTSD and depression symptoms than the C allele carriers. However, when trauma exposure was severe, the TT genotype carriers and C allele carriers did not differ in the risk of developing high co-occurring PTSD and depressive symptoms. The other three SNPs demonstrated no significant effects. Moreover, the rs3800373-rs9296158-rs1360780-rs9470080 haplotype A-G-C-T was found significantly associated with combined PTSD-depression symptoms. Conclusion: Our findings support the genetic basis of phenotypic heterogeneity in people exposed to trauma. Furthermore, the results reveal the possibility that the variants of FKBP5 gene may be associated with depression-PTSD comorbidity.

Depressive symptoms, cognitive decline, and risk of AD in older persons

Neurology ◽  
2002 ◽  
Vol 59 (3) ◽  
pp. 364-370 ◽  
Author(s):  
R. S. Wilson ◽  
L. L. Barnes ◽  
C. F. Mendes de Leon ◽  
N. T. Aggarwal ◽  
J. S. Schneider ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Cognitive Decline ◽  
Older Persons

scholarly journals Longitudinal Association Between Cognition and Depression in Patients With Late-Life Depression: A Cross-Lagged Design Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhangying Wu ◽  
Xiaomei Zhong ◽  
Qi Peng ◽  
Ben Chen ◽  
Min Zhang ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Structural Equation ◽  
Late Life ◽  
Equation Model ◽  
Depression Symptoms ◽  
Information Processing Speed ◽  
Late Life Depression ◽  
Cross Sectional ◽  
Two Factors ◽  
Longitudinal Association

Objectives: Although previous studies have extensively confirmed the cross-sectional relationship between cognitive impairment and depression in depressed elderly patients, the findings of their longitudinal associations are still mixed. The purpose of this study was to explore the two-way causal relationship between depression symptoms and cognition in patients with late-life depression (LLD).Methods: A total of 90 patients with LLD were assessed across two time points (baseline and 1-year follow up) on measures of 3 aspects of cognition and depressive symptoms. The data were then fitted to a structural equation model to examine two cross-lagged effects.Results: Depressive symptoms predicted a decline in executive function (β = 0.864, p = 0.049) but not vice versa. Moreover, depressive symptoms were predicted by a decline in scores of working memory test (β = −0.406, p = 0.023), respectively. None of the relationships between the two factors was bidirectional.Conclusion: These results provide robust evidence that the relationship between cognition and depressive symptoms is unidirectional. Depressive symptoms may be a risk factor for cognitive decline. The decrease of information processing speed predicts depressive symptoms.

scholarly journals Do severity and duration of depressive symptoms predict cognitive decline in older persons? Results of the Longitudinal Aging Study Amsterdam

2004 ◽  
Vol 16 (3) ◽  
pp. 226-232 ◽  
Author(s):  
Hannie C. Comijs ◽  
Theo van Tilburg ◽  
Sandra W. Geerlings ◽  
Cees Jonker ◽  
Dorly J. H. Deeg ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Cognitive Decline ◽  
Older Persons ◽  
Longitudinal Aging Study Amsterdam ◽  
Aging Study ◽  
Longitudinal Aging Study

scholarly journals Successful Aging and Longevity in Older Old Women: The Role of Depression and Cognition

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Daniel Paulson ◽  
Mary Elizabeth Bowen ◽  
Peter A. Lichtenberg
Keyword(s):  
Depressive Symptoms ◽  
Cognitive Decline ◽  
Successful Aging ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Cognitive Status ◽  
Depression Symptoms ◽  
Aging Theory ◽  
Free Women

Based in successful aging theory and terminal cognitive drop research, this paper investigates cerebrovascular burden (CVB), depressive symptoms, and cognitive decline as threats to longevity. A subsample of stroke-free women over the age of 80 was identified in the Health and Retirement Survey (years 2000–2008). Mortality at 2, 6, and 8 year intervals was predicted using CVB (diabetes, heart disease, hypertension), depressive symptoms (Center for Epidemiological Studies Depression Scale), and cognitive decline (decline of 1 standard deviation or more on the 35-point Telephone Interview for Cognitive Status over 2 years). At most waves (2002, 2004, and 2006) mortality was predicted by CVB, depressive symptoms, and cognitive drop measured 2 years prior. CVB and depressive symptoms at the 2000 wave predicted mortality at 6 and 8 years. Older women with the greatest longevity had low CVB, robust cognitive functioning, and few depression symptoms, supporting successful aging theory and terminal cognitive drop.

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 Anhedonia sub-components and reward and effort learning for primary and secondary rewards in young people with depression symptoms

2021 ◽  
Author(s):  
Anna-Lena Frey ◽  
Siyabend Kaya ◽  
Irina Adeniyi ◽  
Ciara McCabe
Keyword(s):  
Depressive Symptoms ◽  
Young People ◽  
Learning Task ◽  
Reward Processing ◽  
Reward Learning ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Treatment Development ◽  
Anticipatory Anhedonia ◽  
Completion Times

Background: Anhedonia, a central depression symptom, is associated with impairments in reward processing. However how the sub-components of reward processing (anticipation, motivation, consummation and learning) are related to depression symptoms is not well understood. In particular, little is known about how effort cost and reward learning is related to anhedonia.Methods: We recruited young people with high (N=50) and low (N=88) depression symptoms and assessed their learning, consummatory, anticipatory, and motivational responses within an effort and reward learning task. To increase the reward attractiveness, especially for younger people, we included not only money (secondary reward), but also chocolate tastes and puppy images (primary rewards).Results: Across all participants, we found that self-reported willingness to exert effort positively correlated with actual effort exertion and negatively with effort completion times. We also observed higher accuracy for reward learning vs. effort learning. Additionally, effort expenditure differed between reward types, although no differences in reward liking were observed. Interestingly, we also found that higher anticipatory anhedonia was associated with lower reward learning accuracy. Limitations: The study assessed only depressive symptoms, not clinically diagnosed major depression. Conclusion: To our knowledge, this is the first study to examine reward and effort learning simultaneously in young people with depression symptoms. Our findings suggest a differentiation between motivational and consummatory responses, as well as between reward and effort learning. Moreover, we show that anticipatory anhedonia is related to reward learning. Understanding the link between objective reward processing and anhedonia sub-types could provide new targets for treatment development.

scholarly journals Effect of Diet on Depression in Adolescents

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Suneeta Pant ◽  
Dr. Madhulata Naya
Keyword(s):  
Depressive Symptoms ◽  
Gender Difference ◽  
Dietary Pattern ◽  
Depression Scale ◽  
Gender Effects ◽  
Depression Symptom ◽  
Traditional Food ◽  
Processed Food ◽  
Symptoms Of Depression

The present study investigated the influence of dietary pattern on depression and also gender effects of depression. A sample of 300 adolescent (150 boys and 150 girls) from Kumaun hills of Uttrakhand state were selected who were taking traditional and mixed food comprise of traditional food with processed food. Dietary pattern were assessed by FFQ and symptoms of depression was tested by IPAT Depression Scale of Krug & Laughlin. Analysis of data indicates that the dietary pattern do not affect the depression symptom, though gender difference for depressive symptoms was significant. Dietary pattern comprised of local millets and vegetables where the study conducted was the reason for less depressed adolescents

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