Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: Several factors may account for inter- and intra-individual variability in cognitive functions, including age, gender, education level, information processing speed, and mood.
Objective: To evaluate the combined contribution of demographic factors, information processing speed, and depressive symptoms to scores on several diagnostic cognitive measures that are commonly used in geriatric neuropsychological practice in Greece.
Methods: Using a cross-sectional study, we established a multivariate general linear model and analyzed the predictive role of age, gender, education, information processing speed (Trail Making Test-Part A), and depressive symptoms (Geriatric Depression Scale-15 Items) on measures of general cognitive status (Mini-Mental State Examination), verbal memory (Rey Auditory Verbal Learning Test), language (Confrontation Naming), and executive functions (Category and Phonemic Fluency, Trail Making Test-Part B) for a sample of 755 healthy, community-dwelling Greek individuals aged 50 to 90 years.
Results: Participant factors significantly but differentially contributed to cognitive measures. Demographic factors and information processing speed emerged as the significant predictors for the majority of the cognitive measures (Mini-Mental State Examination; Rey Auditory Verbal Learning Test; Confrontation Naming; Category and Phonemic Fluency; Trail Making Test-Part B), whereas depressive symptoms significantly predicted verbal memory and semantic fluency measures (Rey Auditory Verbal Learning Test and Category Fluency).
Conclusions: Clinicians should consider participant demographics, underlying slowing of processing speed, and depressive symptoms as potential confounding factors in cognitive measures. Our findings may explain the observed inter- and intra-individual variability in cognitive functions in the elderly population.
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Source |
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http://dx.doi.org/10.1097/WNN.0000000000000211 | DOI Listing |
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