scholarly journals Network Structures of Symptoms From the Zung Depression Scale

2020 ◽  
pp. 003329412094211
Author(s):  
Giovanni Briganti ◽  
Marco Scutari ◽  
Paul Linkowski
Keyword(s):  
Network Analysis ◽  
Depression Scale ◽  
Clinical Intervention ◽  
Male Students ◽  
Clinical Samples ◽  
Depression Symptoms ◽  
Network Structures ◽  
French Speaking ◽  
Node Connectivity ◽  
Zung Depression Scale

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 (“My mind is as clear as it used to be”) is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

scholarly journals Network structures of symptoms from the Zung Depression Scale

2020 ◽  
Author(s):  
Giovanni Briganti ◽  
Marco Scutari ◽  
Paul Linkowski
Keyword(s):  
Network Analysis ◽  
Depression Scale ◽  
Clinical Intervention ◽  
Male Students ◽  
Clinical Samples ◽  
Depression Symptoms ◽  
Network Structures ◽  
French Speaking ◽  
Node Connectivity ◽  
Zung Depression Scale

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 (“My mind is as clear as it used to be”) is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

scholarly journals A network approach on the relation between apathy and depression symptoms with dementia and functional disability

2019 ◽  
Vol 31 (11) ◽  
pp. 1655-1663 ◽  
Author(s):  
Lennard L. van Wanrooij ◽  
Denny Borsboom ◽  
Eric P. Moll van Charante ◽  
Edo Richard ◽  
Willem A. van Gool
Keyword(s):  
Network Analysis ◽  
Functional Disability ◽  
Geriatric Depression Scale ◽  
Depression Scale ◽  
Analysis Data ◽  
Future Research ◽  
Depression Symptoms ◽  
Dementia Risk ◽  
Strongly Connected ◽  
Sum Scores

ABSTRACTBackground:Studies on the association between depression and dementia risk mostly use sum scores on depression questionnaires to model symptomatology severity. Since individual items may contribute differently to this association, this approach has limited validity.Methods:We used network analysis to investigate the functioning of individual Geriatric Depression Scale (GDS-15) items, of which, based on studies that used factor analysis, 3 are generally considered to measure apathy (GDS-3A) and 12 depression (GDS-12D). Functional disability and future dementia were also included in our analysis. Data were extracted from 3229 participants of the Prevention of Dementia by Intensive Vascular care trial (preDIVA), analyzed as a single cohort, yielding 20,542 person-years of observation. We estimated a sparse network by only including connections between variables that could not be accounted for by variance in other variables. For this, we used a repeated L1 regularized regression procedure.Results:This procedure resulted in a selection of 59/136 possible connections. GDS-3A items were strongly connected to each other and with varying strength to several GDS-12D items. Functional disability was connected to all three GDS-3A items and the GDS-12D items “helplessness” and “worthlessness”. Future dementia was only connected to the GDS-12D item “memory problems”, which was in turn connected to the GDS-12D items “unhappiness” and “helplessness” and all three GDS-3A items.Conclusion:Network analysis reveals interesting relationships between GDS items, functional disability and dementia risk. We discuss what implications our results may have for (future) research on the associations between depression and/or apathy with dementia.

scholarly journals Item and domain network structures of the Resilience Scale for Adults in 675 university students

2019 ◽  
Vol 29 ◽  
Author(s):  
G. Briganti ◽  
P. Linkowski
Keyword(s):  
Network Analysis ◽  
Social Competence ◽  
University Students ◽  
Protective Factors ◽  
Network Structure ◽  
Family Cohesion ◽  
Clinical Intervention ◽  
Resilience Scale ◽  
French Speaking ◽  
Original Number

Abstract Aims The Resilience Scale for Adults (RSA) is a questionnaire that measures protective factors of mental health. The aim of this paper is to perform a network analysis of the RSA in a dataset composed of 675 French-speaking Belgian university students, to identify potential targets for intervention to improve protective factors in individuals. Methods We estimated a network structure for the 33-item questionnaire and for the six domains of resilience: perception of self, planned future, social competence, structured style, family cohesion and social competence. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. An exploratory graph analysis (EGA) was performed to detect communities in the network: the number of communities detected being different than the original number of factors proposed in the scale, we estimated a new network with the resulting structure and verified the validity of the new construct which was proposed. We provide the anonymised dataset and code in external online materials (10.17632/64db36w8kf.2) to ensure complete reproducibility of the results. Results The network composed of items from the RSA is overall positively connected with strongest connections arising among items from the same domain. The domain network reports several connections, both positive and negative. The EGA reported the existence of four communities that we propose as an additional network structure. Node predictability estimates show that connectedness varies among the items and domains of the RSA. Conclusions Network analysis is a useful tool to explore resilience and identify targets for clinical intervention. In this study, the four domains acting as components of the additional four-domain network structure may be potential targets to improve an individual's resilience. Further studies may endeavour to replicate our findings in different samples.

scholarly journals Symptoms of mood disorders in family carers of older people with dementia who experience caregiver burden: a network approach

2020 ◽  
Vol 49 (4) ◽  
pp. 628-633
Author(s):  
Alex Bacadini França ◽  
Adam Lee Gordon ◽  
Rajvinder Samra ◽  
Evelise Saia Rodolpho Duarte ◽  
Alessandro Ferrari Jacinto
Keyword(s):  
Network Analysis ◽  
Older People ◽  
Depression Scale ◽  
Anxiety Symptoms ◽  
Warning Signs ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Depression And Anxiety ◽  
Family Carers ◽  
People With Dementia

Abstract Background informal carers of people with dementia are at greater risk of anxiety and depressive disorders if they find caregiving to be a burden. The aim of this study was to use a network analysis of cross-sectional data to investigate the relationships between anxiety and depressive symptoms in family carers of older people with dementia who experience burden. Methods sixty family carers exhibiting high levels of burden using the Zarit Burden Interview were included in the study. Participants completed the Hospital Anxiety and Depression Scale. The network analysis identified the depression and anxiety symptom network using features including a topological graph, network centrality metrics and community analysis. The network was estimated through the graphical LASSO technique in combination with a walktrap algorithm to obtain the clusters within the network and the connections between the nodes (symptoms). A directed acyclic graph was generated to model symptom interactions. Results the resulting network architecture shows important bridges between depression and anxiety symptoms. Lack of pleasure and loss of enjoyment were identified as potential gateway symptoms to other anxiety and depression symptoms and represent possible therapeutic targets for psychosocial interventions. Fear and loss of optimism were highly central symptoms, indicating their importance as warning signs of more generalised anxiety and depression. Conclusions this network analysis of depressive and anxiety symptoms in overburdened family carers provides important insights as to what symptoms may be the most important targets for behavioural interventions.

scholarly journals Item and domain network structures of the Resilience Scale for Adults in 675 university students

2019 ◽  
Author(s):  
Giovanni Briganti ◽  
Paul Linkowski
Keyword(s):  
Network Analysis ◽  
Social Competence ◽  
University Students ◽  
Protective Factors ◽  
Network Structure ◽  
Family Cohesion ◽  
Clinical Intervention ◽  
Resilience Scale ◽  
French Speaking ◽  
Original Number

Aims: The Resilience Scale for Adults (RSA) is a questionnaire that measures protective factors of mental health. The aim of this paper is to perform a network analysis of the Resilience Scale for Adults (RSA) in a dataset composed of 675 French-speaking Belgian university students, to identify potential targets for intervention to improve protective factors in individuals.Methods: We estimated a network structure for the 33-item questionnaire and for the six domains of resilience: perception of self, planned future, social competence, structured style, family cohesion and social competence. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. An Exploratory Graph Analysis (EGA) was performed to detect communities in the network: the number of communities detected being different than the original number of factors proposed in the scale, we estimated a new network with the resulting structure and verified the validity of the new construct which was proposed. We provide the anonymized dataset and code in the supplementary materials to ensure complete reproducibility of the results.Results: The network composed of items from the RSA is overall positively connected with strongest connections arising among items from the same domain. The domain network reports several connections, both positive and negative. The EGA reported the existence of four communities that we propose as an additional network structure. Node predictability estimates show that connectedness varies among the items and domains of the RSA.Conclusions: Network analysis is a useful tool to explore resilience and identify targets for clinical intervention. In this study, the four domains acting as components of the additional four-domain network structure may be potential targets to improve an individual’s resilience. Further studies may endeavor to replicate our findings in different samples.

Are Three Items Sufficient to Measure Sense of Coherence?

2018 ◽  
Vol 34 (4) ◽  
pp. 229-237 ◽  
Author(s):  
Francesca Chiesi ◽  
Andrea Bonacchi ◽  
Caterina Primi ◽  
Alessandro Toccafondi ◽  
Guido Miccinesi
Keyword(s):  
Sense Of Coherence ◽  
Convergent Validity ◽  
Clinical Sample ◽  
Clinical Samples ◽  
Depression Symptoms ◽  
Time Saving ◽  
Positive Role ◽  
Patients With Cancer ◽  
Measure Sense

Abstract. The present study aimed at evaluating if the three-item sense of coherence (SOC) scale developed by Lundberg and Nystrom Peck (1995) can be effectively used for research purpose in both nonclinical and clinical samples. To provide evidence that it represents adequately the measured construct we tested its validity in a nonclinical (N = 658) and clinical sample (N = 764 patients with cancer). Results obtained in the nonclinical sample attested a positive relation of SOC – as measured by the three-item SOC scale – with Antonovsky’s 13-item and 29-item SOC scales (convergent validity), and with dispositional optimism, sense of mastery, anxiety, and depression symptoms (concurrent validity). Results obtained in the clinical sample confirmed the criterion validity of the scale attesting the positive role of SOC – as measured by the three-item SOC scale – on the person’s capacity to respond to illness and treatment. The current study provides evidence that the three-item SOC scale is a valid, low-loading, and time-saving instrument for research purposes on large sample.

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

scholarly journals Bidirectional Association Between Knee Osteoarthritis and Depressive Symptoms: A Nationwide Cohort Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 220-220
Author(s):  
Han Lu ◽  
Shaomei Shang ◽  
Limin Wang ◽  
Hongbo Chen
Keyword(s):  
Cohort Study ◽  
Depressive Symptoms ◽  
Knee Osteoarthritis ◽  
Proportional Hazards ◽  
Depression Scale ◽  
Cox Proportional Hazards ◽  
Depression Symptoms ◽  
Physical And Mental Health ◽  
Bidirectional Association ◽  
Bidirectional Relationship

Abstract Both knee osteoarthritis (KOA) and depressive symptoms are common health issues affecting the quality of life of old adults. Although it is presumed that KOA has a bidirectional relationship with the depressive symptoms, no cohort study has proven it. This is the first study to determine the strength of association for the bidirectional relationship between KOA and depressive symptoms. Data were gathered from the nationally survey of China Health and Retirement Longitudinal Study in 2011-2015. The presence of depressive symptoms was defined by the 10-item Center for Epidemiologic Studies Depression Scale score of 10 or higher. The adjusted Cox proportional hazards regression model was conducted to estimate hazards ratios (HRs). Controlled covariates include gender, age, education, marital status, residence, number of chronic diseases, and disability. The analysis of KOA predicting the depressive symptoms onset consisted of 4,377 participants free from depressive symptoms at baseline. During 4 years follow-up, diagnosed KOA participants were more likely to have depressive symptoms than their peers without KOA (HR = 1.50, 95% CI: 1.23-1.83). The parallel analysis of depressive symptoms predicting KOA onset included 6,848 participants without KOA at baseline, those with depressive symptoms had a higher relative risk of developing KOA (HR = 1.64, 95% CI: 1.41-1.92). Our results provide compelling evidence that the KOA-depressive symptoms association is bidirectional, highlighting the importance of evaluating the relationship between physical and mental health among older people. Particularly, taking this association into consideration in the risk assessment and primary prevention of KOA and depression symptoms.

scholarly journals Caregiver burden versus intensity of anxiety and depression symptoms in parents of children with cerebral palsy as well as factors potentially differentiating the level of burden: a cross-sectional study (Poland)

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e036494
Author(s):  
Barbara Gugała
Keyword(s):  
Cerebral Palsy ◽  
Health Status ◽  
Caregiver Burden ◽  
Depression Scale ◽  
Secondary Outcome ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Emotional Involvement ◽  
Children With Cerebral Palsy ◽  
General Strain

ObjectivesTo assess the relationship between caregiver burden and severity of symptoms of anxiety/depression in parents of children with cerebral palsy (CP), and to identify factors differentiating the level of caregiver burden.SettingRegional rehabilitation centres in South-Eastern Poland.ParticipantsThe study involved 190 parents of children with CP, that is, 138 women and 52 men.Primary and secondary outcome measuresCaregiver burden was assessed using Caregiver Burden Scale (CBS), while the intensity of anxiety and depression symptoms was measured using Hospital Anxiety and Depression Scale (HADS). Potential predictors were examined using Gross Motor Function Classification System for Cerebral Palsy (GMFCS), Barthel Index (BI) as well as a questionnaire focusing on the characteristics of the child, the parent and the family. The analyses applied Pearson’s linear correlation coefficient as well as multiple regression analysis.ResultsAll the CBS measures are significantly correlated to HADS-A (anxiety) and HADS-D (depression). Intensity of anxiety is most visibly linked to CBS measures of disappointment and environment (p<0.0001), while severity of depression is related to emotional involvement and general strain (p<0.0001). The factors differentiating caregiver burden measure in the subscales of general strain (p<0.0001) and social isolation (p<0.0001) include the child’s age and BI, and the parent’s health status; in the subscale of disappointment (p<0.0001)—the child’s age, BI, GMFCS, as well as the parent’s age and health status; in the subscale of emotional involvement (p=0.0007)—BI, and the parent’s health status; in the subscale of environment (p=0.0002)—the child’s age and BI.ConclusionsThere is a positive linear relationship between the caregiver burden measures and severity of anxiety and depression. Effort should be made to relieve caregiver burden in parents of children with CP.

scholarly journals Factor Structure of the Edinburgh Postnatal Depression Scale in a Sample of Postpartum Slovak Women

2021 ◽  
Vol 18 (12) ◽  
pp. 6298
Author(s):  
Zuzana Škodová ◽  
Ľubica Bánovčinová ◽  
Eva Urbanová ◽  
Marián Grendár ◽  
Martina Bašková
Keyword(s):  
Factor Structure ◽  
Postnatal Depression ◽  
Convergent Validity ◽  
Edinburgh Postnatal Depression Scale ◽  
Negative Impact ◽  
Depression Scale ◽  
Depression Symptoms ◽  
Three Dimensional Model

Background: Postpartum depression has a negative impact on quality of life. The aim of this study was to examine the factor structure and psychometric properties of the Slovak version of the Edinburgh Postnatal Depression Scale (EPDS). Methods: A paper and pencil version of the 10-item EPDS questionnaire was administered personally to 577 women at baseline during their stay in hospital on the second to fourth day postpartum (age, 30.6 ± 4.9 years; 73.5% vaginal births vs. 26.5% operative births; 59.4% primiparas). A total of 198 women participated in the online follow-up 6–8 weeks postpartum (questionnaire sent via e-mail). Results: The Slovak version of the EPDS had Cronbach’s coefficients of 0.84 and 0.88 at baseline (T1) and follow-up, respectively. The three-dimensional model of the scale offered good fit for both the baseline (χ2(df = 28) = 1339.38, p < 0.001; CFI = 0.99, RMSEA = 0.02, and TLI = 0.99) and follow-up (χ2(df = 45) = 908.06, p < 0.001, CFI = 0.93, RMSEA = 0.09, and TL = 0.90). A risk of major depression (EPDS score ≥ 13) was identified in 6.1% in T1 and 11.6% in the follow-up. Elevated levels of depression symptoms (EPDS score ≥ 10) were identified in 16.7% and 22.7% of the respondents at baseline and follow-up, respectively. Conclusions: The Slovak translation of the EPDS showed good consistency, convergent validity, and model characteristics. The routine use of EPDS can contribute to improving the quality of postnatal health care.

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