The Factor Structure in the Cognitive Battery of the Structured Interview for the Diagnosis of Dementia of the Alzheimer Type, Multi-Infarct Dementia, and Dementias of Other Aetiology

2001 ◽  
Vol 13 (3) ◽  
pp. 311-323 ◽  
Author(s):  
Martin Kockler ◽  
Reinhard Heun
Keyword(s):  
Principal Component Analysis ◽  
Factor Structure ◽  
Short Term Memory ◽  
Principal Component ◽  
Component Analysis ◽  
Structured Interview ◽  
Elderly Subjects ◽  
Alzheimer Type ◽  
Diagnosis Of Dementia ◽  
Icd 10

The Structured Interview for the Diagnosis of Dementia of the Alzheimer Type, Multi-Infarct Dementia, and Dementias of Other Aetiology (SIDAM) includes the Mini-Mental State Examination extended by a number of additional items and allows the diagnosis of dementia according to ICD-10 and DSM-III-R criteria. The authors proposed to summarize selected items to form syndrome scores. These syndrome scores are supposed to measure different aspects of cognition. However, these syndrome scores have not been empirically confirmed. The present article presents a principal component analysis performed on the SIDAM test performances of 456 elderly subjects. The subjects were recruited in the course of a family study on dementia of the Alzheimer's type and depression in the elderly. One hundred four of these subjects met the criteria of dementia according to the ICD-10 criteria. Thus, the sample represnets the population in which the SIDAM is frequently used in routine clinical and epidemiological studies. We found a six-factor structure accounting for 57.1% of the variance with some similarities to the predefined structure of different syndrome scores proposed by the authors of the SIDAM. The first factor found in principal component analysis represented different higher cortical functions that all depend on language and comprehension. Three factors covered three different aspects of memory, i.e., orientation for time, orientation for place, and short-term memory. One factor represented visuoconstructive skills and, finally, there was a factor representing intellectual abilities and education. This empirically found factor structure characterizes the dimensions of cognitive deficits in demented subjects measured by the SIDAM. Syndrome scores should reflect these dimensions. Consequently, we propose to consider the empirically found factor structure in a new version of the SIDAM.

scholarly journals Prevalence of dementia according to DSM–III–R and ICD–10

2001 ◽  
Vol 179 (3) ◽  
pp. 250-254 ◽  
Author(s):  
Steffi G. Riedel-Heller ◽  
Anja Busse ◽  
Conny Aurich ◽  
Herbert Matschinger ◽  
Matthias C. Angermeyer
Keyword(s):  
Age Groups ◽  
The Elderly ◽  
Structured Interview ◽  
Population Based ◽  
Alzheimer Type ◽  
Dementia Diagnosis ◽  
Case Definitions ◽  
Diagnosis Of Dementia ◽  
Icd 10 ◽  
Prevalence Of Dementia

BackgroundThe prevalence of dementia diagnosis according to ICD–10 and DSM–III–R in population surveys remains poorly understood.AimsTo report and compare prevalence rates according to DSM–III–R and ICD–10.MethodA population-based sample (n=1692, age 75+ years) was investigated by a Structured Interview for Diagnosis of Dementia of Alzheimer Type, Multiinfarct Dementia and Dementia of other Aetiology according to DSM–III–R and ICD–10 (SIDAM).ResultsWhereas 17.4% (95% CI=15.9–19.5) of individuals aged 75+ years suffer from dementia according to DSM–III–R, only 12.4% (95% CI=10.6–14.2) are diagnosed as having dementia according to ICD–10. The results revealed lower ICD–10 rates in all investigated age groups. The largest differences appear in the oldest of the elderly.ConclusionsThe ICD–10 sets a higher threshold for dementia diagnosis. Larger differences in the eldest age groups might reflect difficulties in applying case definitions, especially in those beyond 90 years old.

scholarly journals Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3881
Author(s):  
William Taylor ◽  
Kia Dashtipour ◽  
Syed Aziz Shah ◽  
Amir Hussain ◽  
Qammer H. Abbasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Elderly People ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Principal Component ◽  
Fall Detection ◽  
Component Analysis ◽  
Detection Systems ◽  
Long Short Term Memory

The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

scholarly journals Factorial structure analysis of the Torrance Test with Portuguese students

2013 ◽  
Vol 30 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Ricardo Primi ◽  
Tatiana de Cássia Nakano ◽  
Maria de Fátima Morais ◽  
Leandro Silva Almeida ◽  
Ana Paula Marques David
Keyword(s):  
Principal Component Analysis ◽  
Internal Structure ◽  
Factor Structure ◽  
Structure Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Factorial Structure ◽  
Grouped Variables

In order to verify the factorial structures of the Torrance verbal and figural tests, two activities of each instrument were applied with 193 students from the 10th and 12th years of education in Portugal. We tried to demonstrate that the collinearity of the fluency and flexibility variables could create methodological artifacts that hinder the understanding of the internal structure underlying the test. The principal component analysis without control of collinearity indicated a solution composed of four basic factors that separeted activities. Controlling for collinearity, we found a new solution, which also contained four factors that, unlike the previous result, grouped variables with similar processes but of different activities. The verbal and figural content is also an important element in the factor structure. This new arrangement makes more sense with the theory that underlies the instruments separating the different processes and content which are being measured by the activities.

A New Systematic Method of Measurement and Diagnosis of “Mild Cognitive Impairment” and Dementia According to ICD-10 and DSM-III-R Criteria

1992 ◽  
Vol 4 (4) ◽  
pp. 203-219 ◽  
Author(s):  
Michael Zaudig
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mini Mental State Examination ◽  
Structured Interview ◽  
Elderly Subjects ◽  
Diagnosis Of Dementia ◽  
Icd 10 ◽  
Study Population ◽  
Method Of Measurement ◽  
State Examination

Normative data were collected in a study population of 150 randomly selected elderly subjects. Using the SIDAM (Structured Interview for the Diagnosis of Dementia of the Alzheimer Type, multi-infarct dementia, and dementias of other etiology according to DSM-III-R and ICD-10), both the dimensional and the categorical aspects of dementia and “mild cognitive impairment” are considered. With the SIDAM score (SISCO) [range 0 (minimum)-55 (maximum, no cognitive impairment)] and the SIDAM Mini-Mental State Examination (MMSE) (range 0–30), appropriate cutoffs for the category of DSM-III-R and ICD-10 dementia and “mild cognitive impairment” were defined. MMSE scores of 0–22 were found to be indicative of DSM-III-R and ICD-10 dementia. For “mild cognitive impairment,” MMSE scores ranged from 23–27 according to a DSM-III-R definition (ICD-10: 23–28). An MMSE score of 22 or less was found to differentiate between DSM-III-R/ICD-10 dementia and “mild cognitive impairment,” with a specificity of 92% (ICD-10: 95.6) and a sensitivity of 96% (ICD-10: 96%). With the SIDAM-based DSM-III-R/ICD-10 diagnoses of dementia as the criterion, the SISCO was 97.3% specific (ICD-10: 99%) and 94% sensitive (ICD-10: 94%) in detecting dementia. A SISCO of 0–33 was highly indicative of DSM-III-R and ICD-10 dementia. For “mild cognitive impairment,” a SISCO between 34–47 (ICD-10: 34–51) was found. The SISCO covers a broader range of cognitive functions that the MMSE and is more useful in detecting even very mild cognitive decline. Furthermore, the newly defined category of “mild cognitive impairment” could be validated successfully by means of GDS Stages 2–3 and CDR Stage 0.5. These findings confirm the value of the SIDAM as a short diagnostic instrument for measurement and diagnosis of dementia and “mild cognitive impairment.”

Factor Structure of the Values in Action Inventory of Strengths-72 in Individuals With Multiple Sclerosis

2021 ◽  
Vol 35 (2) ◽  
pp. 94-105
Author(s):  
Muna Bhattarai ◽  
Yunzhen Huang ◽  
Yazmin Castruita Rios ◽  
Susan Miller Smedema
Keyword(s):  
Multiple Sclerosis ◽  
Principal Component Analysis ◽  
Factor Structure ◽  
Character Strengths ◽  
Reliability And Validity ◽  
Principal Component ◽  
Internal Consistency Reliability ◽  
Component Analysis ◽  
Rehabilitation Professionals ◽  
Quantitative Descriptive

BackgroundResearch on character strengths in individuals with multiple sclerosis (MS) is lacking. Moreover, measures of character strengths have not been validated in this population, limiting the opportunities for further investigation.ObjectiveTo investigate the factor structure of the Values in Action Inventory of Strengths (VIA)-72 in a sample of individuals with MS.MethodsThis quantitative descriptive study included 624 individuals with MS. The factor structure of the VIA-72 was examined using principal component analysis. The internal consistency reliability and validity were assessed by computing Cronbach's alphas and correlations of the VIA-72 with other psychosocial constructs, respectively.FindingsThe principal component analysis revealed an interpretable four-factor structure of the VIA-72: intellectual and emotional strengths, temperance, transcendence, and interpersonal strengths. The factors significantly correlated with measures of quality of life, resilience, social support, depression, fatigue, and personality traits, indicating good concurrent validity. The four factors did not completely align with Peterson and Seligman's (2004) classification system.ConclusionsThe four-factor structure of the VIA-72 seems to be interpretable, valid, and reliable. Measures like VIA-72 may help rehabilitation professionals learn more about the nature and development of character strengths among persons with MS and suggest ways to cultivate it. Further studies are indicated to confirm the factor structures and psychometric properties of this scale in persons with MS.

scholarly journals Construct validity of multidimensional personality questionnaire (MPQ)

Psihologija ◽  
2007 ◽  
Vol 40 (2) ◽  
pp. 211-228
Author(s):  
Dusanka Mitrovic ◽  
Snezana Smederevac
Keyword(s):  
Principal Component Analysis ◽  
Factor Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Higher Order ◽  
Negative Emotionality ◽  
Personality Questionnaire ◽  
Reinforcement Sensitivity Theory ◽  
Assumed Similarity ◽  
Multidimensional Personality Questionnaire

The paper presents two studies aimed at the examination of the factor structure of The Multidimensional Personality Questionnaire (MPQ) and joint factor structure of the scales of MPQ and SPSRQ (The Sensitivity to Punishment and Sensitivity to Reward Questionnaire). In the first research conducted on the sample of 1127 participants of both sexes, age 18 to 67, the results of the principal component analysis of the MPQ scales point to the existence of three higher-order dimensions, named General Adaptedness, Psychopathic Tendencies and Negative Emotionality. These dimensions correspond to the dimensions of the Eysenck?s PEN model to the greater extent than they achieve the assumed similarity with the dimensions of the Reinforcement Sensitivity Theory. In the second study conducted on the sample of 199 respondents of both sexes, age 18 to 59, the results of the joint principal component analysis of the MPQ and SPSRQ scales point to the existence of three higher order dimensions, which correspond to the Tellegen?s Positive Emotionality, Negative Emotionality, and Constraint. .

Incidence of Dementia According to DSM–III–R and ICD–10

2001 ◽  
Vol 179 (3) ◽  
pp. 255-260 ◽  
Author(s):  
Steffi G. Riedel-Heller ◽  
Anja Busse ◽  
Conny Aurich ◽  
Herbert Matschinger ◽  
Matthias C. Angermeyer
Keyword(s):  
Structured Interview ◽  
Incidence Rates ◽  
Annual Incidence Rate ◽  
Alzheimer Type ◽  
Dementia Of Alzheimer Type ◽  
Case Definitions ◽  
Diagnosis Of Dementia ◽  
Icd 10 ◽  
The Impact

BackgroundThe impact of different case definitions on incidence rates remains unclear.AimsTo compare incidence rates of dementia according to DSM–III–Rand ICD–10.MethodA two-wave community study was conducted (n=1692, age 75+ years follow-up period 1.6 years). Cognitive function was assessed by the Structured Interview for Diagnosis of Dementia of Alzheimer Type, Multiinfarct Dementia and Dementia of other Aetiology according to ICD–10 and DSM–III–R (SIDAM).ResultsThe annual incidence rate for dementia by applying different case definitions was found to be quite similar (DSM–III–R: 47.4 (95% Cl=36.1–61.2) per 1000 person-years; ICD–10: 45.8 (95% Cl= 35.0–59.0) per 1000 person-years). Age-specific incidence rates increase steeply with age.ConclusionsThe impact of different case definitions on incidence rates of dementia appears limited if case definitions and case-finding procedures at baseline and follow-up are applied consistently.

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