Assessment tools for dementia and depression in older migrants
Bernadette N. Kumar, Esperanza Diaz in Migrant Health, 2019
The basic cognitive processes are universal, including the foundations for forming memories, problem solving, developing language skills, and navigating your surroundings. However, cultural differences exist in the situations to which cognitive processes are applied. Culture prescribes what should be learned, and at what age, and by which gender. Consequently, different cultural environments lead to the development of different patterns of abilities. Cognitive abilities usually measured in cognitive tests represent, at least in their content, learned abilities the scores of which correlate with the subjects’ learning opportunities and contextual experiences throughout a life course. Cultural influences have been described on a variety of cognitive abilities, including perceptual abilities, spatial abilities, memory, language, abstraction, and attention (13).
Working with Korean American Families
Gwen Yeo, Linda A. Gerdner, Dolores Gallagher-Thompson in Ethnicity and the Dementias, 2018
Several cognitive tests are available that can be taken by patients and caregivers to screen those at risk of dementia. Because a large proportion of KA adults are foreign-born immigrants (77%) and have limited English proficiency (41%) (U.S. Census, 2017), culturally and linguistically appropriate screening is necessary. The Clinical Research Center for Dementia of South Korea (Ku et al., 2011) lists several Korean versions of standardized screening tools for dementia. In the clinical settings, the most widely used cognitive test with KA patients is the Mini-Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975), which has been translated and validated for use with Koreans (Kim et al., 2010). A short version of MMSE is also available in Korean, which has been found to be more accurate and less influenced by demographic characteristics (Choe et al., 2014). Other Korean versions of standardized screening tools available include the Clinical Dementia Rating Scale (Choi et al, 2001), Hasegawa Dementia Scale (Yang, 2004) and the Korean-Montreal Cognitive Assessment (Kang, Park, Yu, & Lee, 2009).
Reliability, Validity, and the Measurement of Change in Serial Assessments of Athletes
Mark R. Lovell, Ruben J. Echemendia, Jeffrey T. Barth, Michael W. Collins in Traumatic Brain Injury in Sports, 2020
Linear regression models have been used to evaluate change on neuropsychological tests (e.g., McSweeny, Naugle, Chelune, & Luders, 1993; Salinsky, Storzbach, Dodrill, & Binder, 2001; Sawrie, Chelune, Naugle & Luders, 1996; Sawrie, Marson, Boothe & Harrell, 1999; Temkin, Heaton, Grant, & Dikmen, 1999). The simplest model is to use time one scores to predict time two scores (i.e., simple linear regression, where Y = a + bX, and “a” equals the point where the regression line crosses the Y-axis and “b” equals the slope of the line). If you imagine athletes test scores on the X-axis and retest scores on the Y-axis, then conceptually the retest scores are regressed on the test scores, and the regression equation is written Y = a + bX, where Y is the predicted retest score and X is the time one score. If additional variables, other than initial score, are related to the retest score, then multiple regression can be used. Multiple regression, in contrast to simple regression, involves generating an equation that includes the pretest score in addition to any other relevant variables that may influence test performance. Age, education, gender, and overall cognitive status are common examples of variables that might influence cognitive test performance. Application of the multiple regression equation allows one to generate an expected time two score for an individual (i.e., predicted X2 = (beta weight * X1) + (beta weight * V1) + ... + (beta weight * Vn) + constant).
On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance
Published in Journal of Applied Statistics, 2023
Jing Kersey, Hani Samawi, Jingjing Yin, Haresh Rochani, Xinyan Zhang
Figure 4 presents five core biomarkers as indicators of AD over the clinical disease stages. The curves depict changes from normal to abnormal in the following five biomarkers [20] over AD's progression. FDG-PET measures tau protein in cerebrospinal fluid or by synaptic dysfunction.Brain atrophy measured by structural MRI.Cognitive tests measure memory loss.Cognitive testsmeasure clinical function.
Application of the P300 potential in cognitive impairment assessments after transient ischemic attack or minor stroke
Published in Neurological Research, 2021
Yaqing Zhang, Haoming Xu, Yuan Zhao, Lei Zhang, Yumei Zhang
This study revealed that CIs exhibited by most TIA or minor stroke patients were related to cognitive control and executive dysfunctions. The majority of TIA/minor stroke patients exhibited VCIND. VCIND patients have particularly prominent functional impairments in many cognitive domains that could be detected by multiple executive function tests and are more sensitive to time-limited executive functions [16]. A recent review reported incidences of CI which could be detected by multiple executive function tests, ranging from 5% to 70% after TIA or minor stroke. Such cognitive tests could be used in evaluation tools to evaluate disease frequency [7]. CIs in TIA/minor stroke patients may not be obvious after early application of neuropsychological scales. In this study, the P300 latency was significantly longer in patients with and without CI than in control participants. These data suggest that TIA/minor stroke patients may not show CIs in the acute stage and that existing neuropsychological scales are unable to diagnose potential CIs at this stage. However, CIs in TIA/minor stroke patients may be detected early by ERPs.
Circadian preference and intelligence – an updated meta-analysis
Published in Chronobiology International, 2021
Péter P. Ujma, Vsevolod Scherrer
Intelligence can be defined as “a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience” (Gottfredson 1997). In psychometric practice, intelligence is estimated from the sum scores or factor scores of cognitive test batteries with a generally abstract content. Psychometric intelligence shows high convergent validity across tests, with manifest correlations of ~0.7–0.8 (Jensen 1980) and latent correlations often approaching 1 (Johnson et al. 2004, 2008). Results in different intelligence tests and even in small ad-hoc batteries of various cognitive tests seem to be affected by the same underlying trait of general intelligence, even if they measure it to a different extent (Major et al. 2011).
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