Background
An increase in the aging population is associated with a higher prevalence of diseases and syndromes in older people, one of which is cognitive disorders. Cognitive impairment is a common neurological disorder in old age and includes a wide range of conditions from mild cognitive impairment (MCI) to advanced dementia [
1]. MCI is a precursor to dementia and is a transition from age-related cognitive decline to more severe cognitive disorders [
2]. Different mechanisms such as amyloid deposition, inflammation, an increase in free radicals, loss of synapses and neurons, and dysfunction of neurotransmitters lead to dementia [
1]. There are about 50 million people in the world with dementia, and ten million new cases are added each year. It is estimated that the number of people with neurocognitive disorder (NCD) will reach 152 million by 2050 [
3].
The definitive diagnosis of dementia is possible only by histopathological examination of brain tissue after death, therefore, most cases are diagnosed based on clinical information [
4]. Clinical paradigms that assess cognitive function range from short memory tests to comprehensive assessment scales [
5]. Cognitive disorders are generally assessed by broader neuropsychological tests [
6]. Traditional cognitive status scales show little sensitivity in distinguishing between the normal range of cognitive function and cognitive impairments [
5] and are influenced by culture, language, and education [
7]. The Mini-Mental State Examination (MMSE) and the Clock Drawing Test (CDT) are diagnostic tests for dementia, whose accuracy is still questionable, because their score changes with age [
8,
9], and which limits the ability to diagnose patients with early-stage dementia and MCI [
10]. It is stated that 3.46% of the MMSE standard deviation is related to age and education [
11]. Some studies have even suggested that these tools be used together for more accuracy [
12,
13]. Montreal Cognitive Assessment (MoCA) is also a tool for screening for MCI, It has been reported to have a sensitivity range of about 86 to 95% [
14,
15]. But all these tools require a minimum of literacy (such as calculating, reading, and writing).
In contrast, the use of visual-verbal scales such as A Quick Test of Cognitive Speed (AQT) that are not influenced by factors such as gender, formal education beyond the acquisition of literacy (Grades 5 to 8), and culture, can distinguish between normal aging and cognitive disorders caused by disease [
16].
The processing speed theory of adult age states that the decrease in processing speed is due to cognitive decline, not to the reduction in or lack of information [
17]. Rapid Automatized Naming (RAN) is the ability to perceive a visual symbol such as letter, Color, and Form, or retrieve it quickly and accurately. Stroop in 1953 designed the first RAN test, the Stroop Color, and Word Test. This test involves, among others, the ability to consistently read the names of colors printed in contrasting colors, thus inhibiting responses to distracting features. Denckla and Rudel in 1976 used continuous naming of numbers, shapes, letters, and colors to evaluate RAN speed. Wiig (1984) designed a Color (C), Form (F), and Color-Form (CF) processing-speed test to probe RAN abilities in children with language disorders [
18]. A Quick Test of Cognitive Speed (AQT) was later designed by Wiig et al. to compare processing speed in adults with clinical diagnoses of dementia and neurotypical age peers [
18‐
21]. AQT is a visual-verbal processing speed test that evaluates aspects of executive function and can be used in a variety of languages and cultures [
5,
16,
22]. AQT measures the speed of perception, retrieval, and naming of basic colors and forms in single-dimension naming and cognitive speed associated with central executive functions (attention, working memory, and set shifting) in dual-dimension naming of color-form combinations. The study showed that a decline in the speed of perception and cognition precedes a decline in linguistic-cognitive abilities in mild to moderate severity of AD [
10].
AQT is a good tool for screening early-stages of dementia. it has good validity and reliability [
23]. AQT was not related to education but was correlated with age in the Italian older adults [
5]. Anderson et al. found that reading time was significantly longer in patients with Dementia with Lewy Bodies (DLB) than in patients with mild Alzheimer’s disease [
19]. Another study found that patients with Parkinson’s disease dementia (PDD) had more changes in processing speed than Alzheimer’s disease [
24]. In a previous study, the sensitivity and specificity of dementia diagnosis for AQT have been 0.78 and 0.67, respectively, and were higher than for MMSE (0.61) and CDT (0.46) [
10]. The test-retest reliability ranges from r = 0.84 to 0.97.
AQT has been tested in many languages and findings indicate that processing speed varies with the syllabic structure of words in a given language or family of language. In English, Danish, and Swedish (Germanic languages), the syllable lengths of the stimulus words are essentially the same and processing-speed times do not differ significantly. For speakers of Italian and Spanish (Romance languages), many of the stimulus words are multisyllabic and the processing speed measures are longer. Therefore, this study aimed to evaluate the test-retest reliability, internal consistency (Cronbach’s alpha), and concurrent validity of A Quick Test of Cognitive Speed (AQT) in screening for mild cognitive impairment and dementia in the Persian language.
Discussion
Gender and education do not affect AQT. Nielsen et al. (2006) indicated no difference in time measures between the men and women, but the AQT time measures were shorter for literate than for illiterate old people [
22]. In this study, AQT time was faster in higher education but was not statistically significant. In a study that evaluated the relationship between the AQT measures and neuropsychological test scores, no relationship was found between age and AQT naming time [
6]. AQT time was not correlated with age in this study. A psychometric study of MMSE scores among the elderly reported significant correlations between MMSE scores and age and education [
8]. These results indicate that AQT is less affected by demographic variables.
The findings showed that AQT has suitable levels of reliability and validity for screening mild cognitive impairment (MCI) and dementia among the elderly. Test-retest reliability showed that the correlation of each subscale by itself after 1 month is above 0.84 and Cronbach’s alpha 0.81 shows that the instrument has an acceptable internal consistency. As a comparison, the test-retest reliability of AQT for detecting early-stage dementia in elderly Japanese was found to be 0.88, which was similar to this study [
23]. Concurrent validity for AQT was assessed with MMSE, which is a standard questionnaire used to assess cognitive status in its various domains. Our findings showed that all AQT subscale measures had a significant correlation with MMSE (r > − 0.70). Because the scoring for these two tests is opposite in value, less time on AQT indicates better cognitive status, whereas higher scores in MMSE indicate optimal cognitive status. In comparison, Nielsen et al. (2007) assessed the relationship between AQT and MMSE and found significant negative correlations between tests that ranged from − 0.60 to − 0.72 (
P = 0.01) [
6]. Similar findings were obtained in a study of Italian adults by Petrazzuoli et al. [
5].
Means and standard deviations for AQT Color, Form, and Color-Form naming times indicate a significant difference between the control, MCI, and dementia groups. Andersson et al. (2007) also found significant differences between naming times for healthy participants and groups with Lewy Bodies’ dementia and AD [
19]. Takahashi et al. (2012) found that the mean AQT times for the healthy control group were two times shorter than for the group with MCI and three times shorter than for the group with dementia [
23]. These differences can be related to many factors such as characteristics of the Japanese language or different levels of severity of the disease.
We used the criterion gold standard (i.e., diagnosis by two geriatric psychiatrists) to determine the cut-off point based on the ROC curve. The cut-off point for distinguishing healthy elderly from elderly with MCI was 43.50 s for the Color subscale with a sensitivity of 0.95 and specificity of 0.73. It was 52 s with a sensitivity of 0.98 and specificity of 0.89 for the Form subscale and 89 s with a sensitivity of 0.98 and specificity of 0.62 for the Color-Form subscale. Takahashi et al. (2012) also used the MMSE scores to determine the cut-off points. The diagnostic cut-off point for the Color-Form subscale for early-stage dementia was approximately 71 to 72 s with a sensitivity of 0.85 and a specificity of 0.76 [
23]. In this study, the cut-off point for the Color subscale for differentiating elderly with MCI with dementia was 62.50 s with a sensitivity of 0.87 and a specificity of 0.78. The cut-off point for Form naming was 111 s with a sensitivity of 0.96 and a specificity of 0.46, and the cut-off point for Color-Form naming was 197.50 s with a sensitivity of 0.91 and a specificity of 0.41. A study with 81 patients of the usefulness of different screening tests for dementia in primary care settings reported that for MMSE the sensitivity was 0.58 and specificity 0.91, for the CDT sensitivity was 0.26 and specificity 0.88, and for AQT the sensitivity was 0.78 and specificity 0.67 [
10]. AQT is a suitable tool for primary care centers due to its sensitivity and specificity.
The limitation of this study was the lower number of elderly people with dementia than other groups, but the strength was the distinction between three different cognitive states.
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