The moderating effect of prognostic expectancy on the relationship between insight, illness attribution and depressive symptoms among people with schizophrenia spectrum disorder

2008 ◽  
Author(s):  
Ngan-chi Ng
Keyword(s):  
Depressive Symptoms ◽  
Moderating Effect ◽  
Spectrum Disorder ◽  
Schizophrenia Spectrum Disorder ◽  
Schizophrenia Spectrum ◽  
Illness Attribution ◽  
The Relationship

What are depressive symptoms in acutely ill patients with schizophrenia spectrum disorder?

2015 ◽  
Vol 30 (1) ◽  
pp. 43-50 ◽  
Author(s):  
R. Schennach ◽  
M. Riedel ◽  
M. Obermeier ◽  
F. Seemüller ◽  
M. Jäger ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Depression Scale ◽  
Spectrum Disorder ◽  
Research Network ◽  
Underlying Structure ◽  
Positive Symptoms ◽  
Naturalistic Study ◽  
Schizophrenia Spectrum Disorder ◽  
Schizophrenia Spectrum ◽  
Study Results

AbstractBackground:Aim was to examine depressive symptoms in acutely ill schizophrenia patients on a single symptom basis and to evaluate their relationship with positive, negative and general psychopathological symptoms.Methods:Two hundred and seventy-eight patients suffering from a schizophrenia spectrum disorder were analysed within a naturalistic study by the German Research Network on Schizophrenia. Using the Calgary Depression Scale for Schizophrenia (CDSS) depressive symptoms were examined and the Positive and Negative Syndrome Scale (PANSS) was applied to assess positive, negative and general symptoms. Correlation and factor analyses were calculated to detect the underlying structure and relationship of the patient’s symptoms.Results:The most prevalent depressive symptoms identified were depressed mood (80%), observed depression (62%) and hopelessness (54%). Thirty-nine percent of the patients suffered from depressive symptoms when applying the recommended cut-off of a CDSS total score of > 6 points at admission. Negligible correlations were found between depressive and positive symptoms as well as most PANSS negative and global symptoms despite items on depression, guilt and social withdrawal. The factor analysis revealed that the factor loading with the PANSS negative items accounted for most of the data variance followed by a factor with positive symptoms and three depression-associated factors.Limitations:The naturalistic study design does not allow a sufficient control of study results for the effect of different pharmacological treatments possibly influencing the appearance of depressive symptoms.Conclusion:Results suggest that depressive symptoms measured with the CDSS are a discrete symptom domain with only partial overlap with positive or negative symptoms.

Add-on Antidepressants in the Naturalistic Treatment of Schizophrenia Spectrum Disorder – When, Who, and How?

2017 ◽  
Vol 50 (04) ◽  
pp. 136-144 ◽  
Author(s):  
Rebecca Schennach ◽  
Michael Obermeier ◽  
Florian Seemüller ◽  
Markus Jäger ◽  
Max Schmauss ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Side Effects ◽  
Acute Treatment ◽  
Negative Symptoms ◽  
Well Being ◽  
Spectrum Disorder ◽  
Positive Symptoms ◽  
Subjective Well Being ◽  
Schizophrenia Spectrum Disorder ◽  
Schizophrenia Spectrum

Abstract The aim of this study was to evaluate antidepressant add-on treatment within the acute treatment of schizophrenia spectrum disorder patients. Antidepressant add-on was evaluated in 365 patients within a naturalistic multicenter study. Patients with/without antidepressant add-on were compared regarding clinical and treatment-related variables, response and remission, and remission of depressive and negative symptoms. The efficacy of antidepressant add-on treatment was furthermore analyzed applying marginal structure models. Twenty-three percent of the patients received antidepressant add-on for a mean duration of 50.28 (33.42) days. Patients with the diagnosis of a schizoaffective disorder, multiple illness episodes, and a longer duration of their illness as well as those with significantly fewer baseline positive symptoms, more negative and depressive symptoms, more side effects, and less subjective well-being were augmented with antidepressants. At discharge no significant effect of antidepressant add-on treatment was observed in terms of a 25% improvement (p=0.2623), a 50% improvement (p=0.3946), remission (p=0.0552), or remission of depressive (p=0.6336) and negative symptoms (p=0.8756). Also, when analyzing marginal structure models considering the diagnostic subgroups, no significant effect was found. Add-on with antidepressants is common. A final recommendation in terms of this strategy’s efficacy cannot be given.

Depressive symptoms in first episode schizophrenia spectrum disorder

2012 ◽  
Vol 134 (1) ◽  
pp. 20-26 ◽  
Author(s):  
S.M. Cotton ◽  
M. Lambert ◽  
B.G. Schimmelmann ◽  
A. Mackinnon ◽  
J.F.M. Gleeson ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Spectrum Disorder ◽  
First Episode ◽  
Schizophrenia Spectrum Disorder ◽  
Schizophrenia Spectrum ◽  
First Episode Schizophrenia

Sleep and cognition in schizophrenia spectrum disorders: A systematic review protocol.

2020 ◽  
Author(s):  
Sean Carruthers ◽  
Gemma Brunetti ◽  
Susan Rossell
Keyword(s):  
Systematic Review ◽  
Mental Illnesses ◽  
Schizophrenia Spectrum Disorders ◽  
Systematic Review Protocol ◽  
Schizophrenia Spectrum Disorder ◽  
Schizophrenia Spectrum ◽  
Review Protocol ◽  
Spectrum Disorders ◽  
Sleep And Cognition ◽  
The Relationship

Schizophrenia spectrum disorders are chronic and debilitating mental illnesses characterised by both cognitive impairments and sleep deficits. In this systematic review protocol, we outline an approach to examine the available literature investigating the relationship between sleep and cognition in individuals with schizophrenia spectrum disorder.

scholarly journals Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

2021 ◽  
pp. 1-11
Author(s):  
J. N. de Boer ◽  
A. E. Voppel ◽  
S. G. Brederoo ◽  
H. G. Schnack ◽  
K. P. Truong ◽  
...  
Keyword(s):  
Negative Symptoms ◽  
Structured Interview ◽  
Diagnostic Value ◽  
Spectrum Disorder ◽  
Psychotic Symptoms ◽  
Schizophrenia Spectrum Disorders ◽  
Schizophrenia Spectrum Disorder ◽  
Speech Technology ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

Abstract Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

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