Abstract

The Montreal Cognitive Assessment (MoCA) is a brief cognitive instrument for screening milder forms of cognitive impairment. The present study aimed to analyze the influence of sociodemographic (age, gender, educational level, marital and employment status, geographic region, geographic localization, and residence area) and health variables (subjective memory complaints of the participant and evaluated by the informant, depressive symptoms, and family history of dementia) on the participants' performance on the MoCA. The investigation was carried out in a Portuguese community-based sample of 650 cognitively healthy adults, who were representative of the distribution observed in the Portuguese population. Educational level and age significantly contributed to the prediction of the MoCA scores, explaining 49% of the variance. Regarding health variables, only the subjective memory complaints of the participant showed a small contribution (9%) to the variance on the MoCA scores. This study contributes a useful approach to understanding MoCA performance, stressing the great impact of education and age on scores.

Introduction

Several studies have demonstrated that performance on screening tests is influenced by sociodemographic variables. It has been widely reported that age and educational level have a significant effect on cognitive screening test performance. With regard to age, older age has been found to significantly increase the probability of obtaining lower scores (Bravo & Hébert, 1997; Gallacher et al., 1999; Han et al., 2008; Langa et al., 2009; Matallana et al., 2011; Mathuranath et al., 2007; Moraes, Pinto, Lopes, Litvoc, & Bottino, 2010; Rossetti, Lacritz, Cullum, & Weiner, 2011). Regarding the educational level, the worst performance has been found among those with lower education levels, and ceiling effects have been observed among highly educated individuals. The magnitude of this effect is strong; therefore, education is invariably considered a criterion for the establishment of normative data for cognitive tests (Bravo & Hébert, 1997; Guerreiro, 1998; Han et al., 2008; Lieberman et al., 1999; Mathuranath et al., 2007; Measso et al., 1993; Moraes et al., 2010; Morgado, Rocha, Maruta, Guerreiro, & Martins, 2010; Nguyen, Black, Ray, Espino, & Markides, 2002; Rossetti et al., 2011). The results regarding gender are more controversial. Some studies have suggested that gender contributes significantly to the explanation of variance on scores of cognitive screening tests (Han et al., 2008; Measso et al., 1993; Mías, Sassi, Masih, Querejeta, & Krawchik, 2007; Ribeiro, Oliveira, Cupertino, Neri, & Yassuda, 2010; Scazufca, Almeida, Vallada, Tasse, & Menezes, 2009), whereas others have not supported this hypothesis (Bertolucci et al., 2001; Lieberman et al., 1999; Mathuranath et al., 2007; Morgado et al., 2010). There has also been a lack of consensus regarding marital status, as some studies have reported greater performances among married persons (Fratiglioni, Wang, Ericsson, Maytan, & Winblad, 2000; Moraes et al., 2010; Nguyen et al., 2002; Ribeiro et al., 2010; Wu, Lan, Chen, Chiu, & Lan, 2011), whereas others have found no influence on cognitive state assessment (Bertolucci et al., 2001; Mías et al., 2007). Information regarding employment status is relatively scarce. One study found better scores among individuals who are currently employed (Moraes et al., 2010) and another reported worse scores among individuals with occupations with low intellectual demands (Anderson, Sachdev, Brodaty, Trollor, & Andrews, 2007). Investigations of geographic variables are complicated, and international inter-study comparison is meaningless due to the specificities of the populations and territories. Some research has reported an IQ discrepancy in different geographical regions of a country (Kaufman, McClean, & Reynolds, 1988; Lynn, 1979), which could be associated with average regional incomes (Almeida, Lemos, & Lynn, unpublished manuscript; McDaniel, 2006). Because there are no Portuguese studies on the influence of geographic variables on cognitive test performance, the inclusion of these variables in the current study assume an exploratory nature.

The influence of health variables on performance at this level of cognitive screening has also been reported in the literature. Co-morbidity of depressive symptoms and cognitive decline is common, and several studies have aimed to clarify the complex interaction between these conditions (Chen, Ganguli, Mulsant, & DeKosky, 1999; Emery & Oxman, 1992; Rovner, Broadhead, Spencer, Carson, & Folstein, 1989). The general tendency toward a poor cognitive performance in the presence of depressive symptoms is well-documented in the literature (Gallacher et al., 1999; Moraes et al., 2010; Nguyen et al., 2002). The manifestation of subjective memory complaints is one of the diagnostic criteria for Mild Cognitive Impairment (Petersen, 2000, 2007), but such complaints are also frequent among healthy elderly populations (Reid & MacLullich, 2006). Additionally, also there is a significant association between anxiety and depressive disorders, which further raises questions about the complex relationship between affective symptoms and memory. Reviewing the literature on the influence of subjective memory complaints on cognitive tests and their predictive value of conversion to dementia is a complex task due to the diversity of methodologies used to evaluate subjective memory complaints and cognitive function. As expected, the results of this research are conflicting. Whereas some studies have reported worse performance on cognitive tests among subjects with memory complaints, other investigations have evidenced that memory complaints are a poor indicator of cognitive function (Reid & MacLullich, 2006). Family history of dementia is a well-known risk factor for Alzheimer's disease; however, few studies have examined the influence of this genetic trait on the cognitive screening tests of healthy subjects. The limited evidence that is available points to a lack of association between these variables (Mías et al., 2007).

The Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) is a recent screening test that was specifically developed to screen for milder forms of cognitive impairment. Although it was initially designed to assess the cognitive states of patients with Mild Cognitive Impairment and Alzheimer's disease, the MoCA is now an extensively validated screening tool for many disorders (e.g., Parkinson's disease, Huntington's disease, HIV, multiple sclerosis). This measure overcomes the limitations of the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975), because it allows a more comprehensive assessment of the major cognitive domains, including the executive functions assessment, and utilizes more complex tasks to measure short-term memory (and with longer delay), language, attention, concentration, working memory, and visuospatial skills. Several studies have reported the good psychometric properties and excellent sensitivity of the MoCA to cognitive impairment, which has driven its rapid international dissemination and recommendation as a cognitive screening tool in various guidelines (Arnold et al., 2007; Chertkow et al., 2007; Gauthier et al., 2011; Hachinski et al., 2006; Ismail, Rajji, & Shulman, 2009; Jacova, Kertesz, Blair, Fisk, & Feldman, 2007; Lonie, Tierney, & Ebmeier, 2009; Zhao, Liu, Shen, & Zhao, 2011). To expand upon this research, new population studies are needed to explore the modulation of MoCA scores by the most significant sociodemographic and health variables, as this analysis has not been done in detail in any country.

Studies of the translation, adaptation, validation, and normative of the MoCA for the Portuguese population were performed by our group (Freitas, Santana, & Simões, 2010; Freitas, Simões, Alves, & Santana, 2011, 2012; Freitas, Simões, Marôco, Alves, & Santana, 2012; Freitas, Simões, Martins, Vilar, & Santana, 2010; Simões et al., 2008). In the present investigation, we aim to analyze the influence of sociodemographic variables (age, gender, educational level, marital status, employment status, geographic region, geographic localization, and residence area) and health variables (depressive symptoms, subjective memory complaints, and family history of dementia) on the participants' performance on the MoCA using a large Portuguese community-based sample, stratified according to the main sociodemographic variables of the population.

Methods

Participants and Procedures

The investigation was carried out in a community-based sample of volunteers who were recruited at national health and social security services and resided in all geographic regions of the Portuguese continental territory. This sample is representative of the Portuguese population and was used in a recent MoCA normative study published by our group (Freitas et al., 2011). The inclusion criteria were as follows: age 25 years and older; native speaker of Portuguese and schooling in Portugal; and the absence of significant motor, visual, or auditory deficits, all of which may influence performance on tests. To ensure that participants were cognitively healthy adults, we also defined the following exclusion criteria: evidence of loss of autonomy in daily living activities; history of alcoholism or substance abuse; relevant neurological or psychiatric diseases or chronic unstable systemic disorders that impact cognition; significant depressive complaints; and medication with a possible impact on cognition (e.g., psychotropic or psycho-active drugs). A psychologist confirmed these general criteria in an interview that included a complete sociodemographic questionnaire, an inventory of current clinical health status, and past habits and medical history. For older participants, this information was also confirmed by a general practitioner, community centre directors, and/or an informant, typically an individual in co-habitation or a close relative. Due to the extensive use of the MoCA in clinical populations, we also established normative data for people 25 years old to allow the use of this instrument with younger patients with other diseases.

After this initial selection, all subjects were required to display normal performance on the assessment battery used in this study (see “Materials”), considering the Portuguese cut-off points, for effective inclusion in the study. Each participant was assessed in a single session by one of two psychologists with expertise in neuropsychological assessment.

From the initial community-based sample of 936 volunteers, 194 subjects (20.73%) were excluded in light of data collected in the interview (most frequent reasons: history of neurological or psychiatric disorder, history of alcohol abuse, and subjective self-evaluation that memory complaints significantly influence day-to-day activities), 58 subjects (6.20%) were excluded due to the presence of significant depressive symptoms (according to the criteria: Geriatric Depression Scale [GDS] score over 20 points), and 34 subjects (3.63%) were excluded due to suspected cognitive impairment based on their performance on the assessment battery and respective Portuguese cutoff points.

Only the subjects who met all of the defined inclusion and exclusion criteria were eligible for the study. The final sample comprised 650 cognitively healthy adults, and stratification according to the sociodemographic variables confirmed that this sample was representative of the distribution observed in the Portuguese population (Table 1).

Table 1.

Sociodemographic characterization and stratification of the sample

  Levels Sample
 
Portugal
 
n Percent n Percent 
Sociodemographic stratification of sample 
Age 25–49 214 33.0 — 
50–64 218 33.5 — 
≥65 218 33.5 — 
Gender Female 408 62.8 3946 52.6 
Male 242 37.2 3559 47.4 
Educational level Primary 256 39.4 2426 36.6 
Middle 170 26.2 2280 34.4 
High 112 17.2 960 14.5 
University 112 17.2 956 14.5 
Geographic region North 251 38.6 2722 36.0 
Center 174 26.8 1794 24.0 
Lisbon 164 25.2 2091 28.0 
Alentejo 44 6.8 577 8.0 
Algarve 17 2.6 321 4.0 
Geographic localization Coast 546 84.0 6379 85.0 
Inland 104 16.0 1126 15.0 
Residence area PUA 446 68.6 5103 68.0 
MUA 112 17.2 1200 16.0 
PRA 92 14.2 1200 16.0 
Others sociodemographic variables 
Marital status Married 489 75.2 — 
Single 161 24.8 — 
Employment status Active 330 50.8 — 
Inactive 320 49.2 — 
  Levels Sample
 
Portugal
 
n Percent n Percent 
Sociodemographic stratification of sample 
Age 25–49 214 33.0 — 
50–64 218 33.5 — 
≥65 218 33.5 — 
Gender Female 408 62.8 3946 52.6 
Male 242 37.2 3559 47.4 
Educational level Primary 256 39.4 2426 36.6 
Middle 170 26.2 2280 34.4 
High 112 17.2 960 14.5 
University 112 17.2 956 14.5 
Geographic region North 251 38.6 2722 36.0 
Center 174 26.8 1794 24.0 
Lisbon 164 25.2 2091 28.0 
Alentejo 44 6.8 577 8.0 
Algarve 17 2.6 321 4.0 
Geographic localization Coast 546 84.0 6379 85.0 
Inland 104 16.0 1126 15.0 
Residence area PUA 446 68.6 5103 68.0 
MUA 112 17.2 1200 16.0 
PRA 92 14.2 1200 16.0 
Others sociodemographic variables 
Marital status Married 489 75.2 — 
Single 161 24.8 — 
Employment status Active 330 50.8 — 
Inactive 320 49.2 — 

Notes: PUA = predominantly urban areas; MUA = moderately urban areas; PRA = predominantly rural areas.

The values (n) of the Portuguese population are expressed in thousands and represent the data of the resident population in continental Portugal aged over 24 years (Instituto Nacional de Estatística, 2010).

Informed consent was obtained from all of the participants after the aims and the procedures of the investigation, and confidentiality requirements were fully explained by a member of the study group. The present research complied with the ethical guidelines for human experimentation stated in the Declaration of Helsinki and was approved by the Portuguese Foundation for Science and Technology and by the Faculty of Psychology and Educational Sciences Scientific Committee.

Materials

The assessment battery for the global assessment of each participant was composed of the following instruments:

  • Complete sociodemographic questionnaire.

  • Inventory of current clinical health status.

  • Inventory of past habits and medical history.

  • MoCA (Nasreddine et al., 2005; Simões et al., 2008), which is a brief cognitive screening instrument that was developed for the screening of milder forms of cognitive impairment. The tool is a one-page test with paper-and-pencil format, and the application time is approximately 10–15 min. A manual provides explicit instructions for administration and an objectively defined scoring system. The maximum score is 30 points, with higher scores indicating better cognitive performance. It evaluates the following eight cognitive domains: executive functions; visuospatial abilities; short-term memory; language; attention, concentration, and working memory; and temporal and spatial orientation. In the current study, the MoCA was not used as a diagnostic tool. Furthermore, the MoCA total score refers to the raw score without the correction point for educational effects proposed in the original study (Nasreddine et al., 2005) because this correction point is not used in the Portuguese population (Freitas et al., 2011).

  • MMSE (Folstein et al., 1975; Guerreiro, 1998), which is the most widely used brief screening instrument for detecting cognitive deficits and, therefore, is not described in detail here.

  • Clinical Dementia Rating scale (CDR; Garret et al., 2008; Hughes, Berg, Danziger, Coben, & Martin, 1982), which is a global staging tool for dementia that is based on the assessment of cognitive function and functional capacity (in six cognitive-behavioral categories: memory, orientation, sense and problem-solving, community activities, home activities and hobbies, and personal care). The scale is administered to the adult/elderly patients and an informant through a semi-structured interview. The CDR was only administered to participants over 49 years of age and when a close informant was available. In the current study, a global score of zero was used as a criterion for inclusion.

  • Irregular Word Reading Test (Teste de Leitura de Palavras Irregulares; Alves, Simões, & Martins, 2009), which is a tool for estimating premorbid intelligence that consists of a list of 46 irregular words that the participant reads.

  • GDS-30 (Barreto, Leuschner, Santos, & Sobral, 2008; Yesavage et al., 1983), which is a brief scale to assess depressive symptoms in adults. It is composed of 30 dichotomous response questions that assess emotional and behavioral symptoms of depression (score range = 0–30).

  • Subjective Memory Complaints scale (SMC; Ginó et al., 2008; Schmand, Jonker, Hooijer, & Lindeboom, 1996). This scale consists of 10 multiple choice items that assess the presence of subjective memory complaints (score range = 0–21). It was administered under two conditions: (i) SMC participants: answered by the participants to evaluate their own subjective memory complaints, and (ii) SMC informants: answered by informants to assess their opinion about the memory capacity of the participant (when a close informant was available).

Variable Definitions and Sample Stratification

To enhance the representativeness of the observed distribution in the Portuguese population, the sample of 650 participants was stratified according to the following sociodemographic variables:

  • age (age intervals were: 25–49 [“young adults”: mean age = 38.12 ± 8.086], 50–64 [“adults”: mean age = 57.12 ± 4.199], and 65 and over [“elderly”: mean age = 71.96 ± 5.433]);

  • gender (women and men);

  • educational level (four educational levels were considered, according to the number of school years successfully completed in the Portuguese education system: 1–4 [primary education], 5–9 [middle school], 10–12 [high school], and over 12 years of education [university/college]; these categories match the divisions in the Portuguese school system);

  • geographic region (Portuguese continental territory is divided into five geographic regions [NUTS-II classification; Instituto Nacional de Estatística, 2010]: North, Centre, Lisbon, Alentejo and Algarve);

  • geographic localization (two geographic localizations were considered: coast and inland);

  • residence area (according to the Types of Urban Areas [Instituto Nacional de Estatística, 2010], categorized into predominantly urban areas, moderately urban areas, and predominantly rural areas). In this study, we also included the following sociodemographic and health variables that were not criteria for sample stratification:

  • marital status (categorized into “single” [single, divorced, or widowed participants] or “married” [married or living in union participants]);

  • employment status (categorized into “active” [participants with an active work situation] or “inactive” [participants unemployed, retired, or domestic]);

  • family history of dementia (only the information about first-degree relatives was considered relevant, and classification was dichotomized into “positive” or :negative”);

  • depressive symptoms (operationalized by the total score on the GDS-30). Once the participants with severe depressive symptoms were excluded, this study analyzed the influence of depressive symptom levels among non-depressed to mildly depressed individuals on MoCA performance;

  • subjective memory complaints (operationalized by the total score on SMC; two conditions were considered: (a) SMC participants and (b) SMC informants).

Statistical Analysis

All data analyses were conducted using the Statistical Package for the Social Sciences, version 17.0 (SPSS, v.17.0). Descriptive statistics were computed for all sociodemographic and health variables. The observed correlations (using the Pearson correlation coefficient; Cohen, 1988), Cronbach's α coefficients, and corrected correlations among measures were also calculated. The differences on the MoCA scores among subgroups stratified according to sociodemographic variables were examined using Student's t-test, analysis of variance, Tukey's HSD, the Bonferroni post hoc test, and analysis of covariance. Partial eta-squared (forumla) was used as an estimate of the effect size (Cohen, 1988). The contribution of the variables age and educational level was the subject of further statistical analysis. The correlation between MoCA scores, age, and education was performed using the Pearson correlation coefficient (r) (Cohen, 1988). To investigate the significance of age (in years) and education (years of schooling successfully completed) as influencing factors of the MoCA, multiple linear regression (MLR) analyses were performed using the enter method. The multicollinearity was examined through tolerance and variance inflation factor statistics (Meyers, Gamst, & Guarino, 2006), and the coefficient of determination (R²) was considered in the analysis of effect size in the regressions (Cohen, 1988). Finally, the influence of health variables on the MoCA performance was investigated using MLR analysis, stepwise method, for variables with significant Pearson correlations. All data for this investigation were collected by two neuropsychologists only, which allowed rigorous data collection and procedures. Therefore, the only variable with missing data was SMC informants, as not all participants had a close informant available (n = 156; 24%). For this variable, the results correspond to 494 participants who had a close informant available.

Results

The final study sample included 650 cognitively healthy participants (mean age = 55.84 ± 15.12, age range = 25–91; mean education = 8.16 ± 4.72, education range = 2–27). The sociodemographic characteristics of the sample are presented in detail in Table 1, taking into account the stratification variables as well as the other sociodemographic variables considered in the study. The distribution of the study sample in several strata is comparable with the distribution of the target Portuguese population.

The observed correlations, Cronbach's α coefficients, and corrected correlations among the different measures are presented in Table 2. Table 3 summarizes the analysis of differences of the MoCA mean scores between the subgroups.

Table 2.

Observed correlations, Cronbach's α coefficients, and corrected correlations among measures

Variables MoCA MMSE TeLPI GDS SMC
 
Participants Informants 
MoCA (.78) 1.10 .73 .27 .39 .07 
MMSE .65** (.45) .64 .24 .35 .07 
TeLPI −.62** −.43** (.92) .16 .24 .10 
GDS −.22** −.15** .14** (.86) .65 .35 
SMC-participants −.31** −.21** .21** .54** (.80) .42 
SMC-informants −.05 −.04 −.08 .28** .32** (.73) 
Variables MoCA MMSE TeLPI GDS SMC
 
Participants Informants 
MoCA (.78) 1.10 .73 .27 .39 .07 
MMSE .65** (.45) .64 .24 .35 .07 
TeLPI −.62** −.43** (.92) .16 .24 .10 
GDS −.22** −.15** .14** (.86) .65 .35 
SMC-participants −.31** −.21** .21** .54** (.80) .42 
SMC-informants −.05 −.04 −.08 .28** .32** (.73) 

Notes: MoCA = Montreal Cognitive Assessment; MMSE = Mini-Mental State Examination; TeLPI = Irregular Word Reading Test; GDS = Geriatric Depression Scale; SMC = Subjective Memory Complaints scale.

Alpha coefficients are presented on the diagonal, observed correlations below the diagonal, and correlations corrected for attenuation above the diagonal.

**p < .01.

Table 3.

Analysis of group differences on the MoCA scores (without control of the effect of covariates)

Variables MoCA M ± SD t/F Post hoc 
Age 
 25–49 26.98 ± 2.548 F(2,647) = 95.130, p < .001 All groups differ. 
 50–64 24.46 ± 3.432 
 ≥65 22.71 ± 3.668 
Gender 
 Female 24.50 ± 3.798 t = 1.80, p = .072 — 
 Male 25.04 ± 3.419 
Educational level 
 Primary 21.73 ± 3.185 F(3,646) = 194.996, p <.001 All groups differ. Significant linear effect: F = 435.895, p < .001 
 Middle 25.65 ± 2.501 
 High 26.77 ± 2.153 
 University 28.04 ± 1.942 
Geographic region 
 A. North 24.22 ± 3.644 F(4,645) = 8.765, p < .001 A ≠ C, D 
 B. Center 24.28 ± 3.841 B ≠ C, D 
 C. Lisbon 25.76 ± 3.395 C ≠ A, B, E 
 D. Alentejo 26.11 ± 2.713 D ≠ A, B, E 
 E. Algarve 22.41 ± 3.658 E ≠ C, D 
Geographic localization 
 Coast 24.80 ± 3.715 t = 1.546, p = .122 — 
 Inland 24.19 ± 3.382 
Residence area 
 PUA 24.93 ± 3.728 F(2,647) = 4.175, p = .016 PUA ≠ PRA 
 MUA 24.60 ± 3.362 
 PRA 23.73 ± 3.604 
Marital status 
 Married 24.40 ± 3.569 t = 3.652, p ≤ .001 — 
 Single 25.61 ± 3.823 
Employment status 
 Active 26.22 ± 2.988 t = 11.781, p ≤ .001 — 
 Inactive 23.13 ± 3.649 
Variables MoCA M ± SD t/F Post hoc 
Age 
 25–49 26.98 ± 2.548 F(2,647) = 95.130, p < .001 All groups differ. 
 50–64 24.46 ± 3.432 
 ≥65 22.71 ± 3.668 
Gender 
 Female 24.50 ± 3.798 t = 1.80, p = .072 — 
 Male 25.04 ± 3.419 
Educational level 
 Primary 21.73 ± 3.185 F(3,646) = 194.996, p <.001 All groups differ. Significant linear effect: F = 435.895, p < .001 
 Middle 25.65 ± 2.501 
 High 26.77 ± 2.153 
 University 28.04 ± 1.942 
Geographic region 
 A. North 24.22 ± 3.644 F(4,645) = 8.765, p < .001 A ≠ C, D 
 B. Center 24.28 ± 3.841 B ≠ C, D 
 C. Lisbon 25.76 ± 3.395 C ≠ A, B, E 
 D. Alentejo 26.11 ± 2.713 D ≠ A, B, E 
 E. Algarve 22.41 ± 3.658 E ≠ C, D 
Geographic localization 
 Coast 24.80 ± 3.715 t = 1.546, p = .122 — 
 Inland 24.19 ± 3.382 
Residence area 
 PUA 24.93 ± 3.728 F(2,647) = 4.175, p = .016 PUA ≠ PRA 
 MUA 24.60 ± 3.362 
 PRA 23.73 ± 3.604 
Marital status 
 Married 24.40 ± 3.569 t = 3.652, p ≤ .001 — 
 Single 25.61 ± 3.823 
Employment status 
 Active 26.22 ± 2.988 t = 11.781, p ≤ .001 — 
 Inactive 23.13 ± 3.649 

Notes: PUA = predominantly urban areas; MUA = moderately urban areas; PRA = predominantly rural areas; M: mean; SD: standard deviation; t: Student's t-test values; F: analysis of variance values; Post hoc: Tukey HSD and Bonferroni post hoc test analyses.

The sociodemographic variables that showed significant group differences (age, educational level, geographic region, area of residence, marital status, and employment status) were targeted for further data analysis. Significant differences in mean age and mean educational level between the subgroups were verified (Table 4). Based on this result, we proceeded with the analysis of covariance to examine whether differences in the MoCA scores remained significant after controlling for the effect of covariates (age and/or educational level) and to estimate the effect size of each variable (Table 5).

Table 4.

Analysis of covariates: group differences in mean age and educational level

Variables Age
 
Educational level
 
M ± DP t/F M ± DP t/F 
Age 
 25–49 —  10.64 ± 4.688 F(2,647) = 62.757, p < .001 
 50–64 7.75 ± 4.289 
 ≥65 6.08 ± 3.989 
Educational level 
 Primary 63.99 ± 10.843 F(3,646) = 57.631, p < .001 — — 
 Middle 53.69 ± 14.454 
 High 48.69 ± 14.523 
 University 47.50 ± 16.008 
Geographic region 
 North 53.53 ± 13.741 F(4,645) = 6.517, p < .001 6.91 ± 3.891 F(4,645) = 15.675, p < .001 
 Center 60.09 ± 14.092 8.50 ± 5.382 
 Lisbon 54.41 ± 16.454 10.20 ± 4.627 
 Alentejo 54.82 ± 18.746 7.59 ± 4.299 
 Algarve 63.12 ± 10.624 4.94 ± 2.703 
Residence area 
 PUA 55.63 ± 14.879 F(2,647) = 2.094, p = .124 8.60 ± 4.871 F(2,647) = 8.450, p < .001 
 MUA 54.43 ± 16.408 7.79 ± 4.298 
 PRA 58.62 ± 14.456 6.46 ± 4.064 
Marital status 
 Married 57.24 ± 13.431 t = 3.527, p = .001 7.65 ± 4.551 t = 4.842, p ≤ .001 
 Single 51.60 ± 18.789 9.70 ± 4.917 
Employment status 
 Active 45.84 ± 11.549 t = 23.147, p ≤ .001 9.81 ± 4.739 t = 9.673, p ≤ .001 
 Inactive 66.16 ± 10.829 6.46 ± 4.065 
Variables Age
 
Educational level
 
M ± DP t/F M ± DP t/F 
Age 
 25–49 —  10.64 ± 4.688 F(2,647) = 62.757, p < .001 
 50–64 7.75 ± 4.289 
 ≥65 6.08 ± 3.989 
Educational level 
 Primary 63.99 ± 10.843 F(3,646) = 57.631, p < .001 — — 
 Middle 53.69 ± 14.454 
 High 48.69 ± 14.523 
 University 47.50 ± 16.008 
Geographic region 
 North 53.53 ± 13.741 F(4,645) = 6.517, p < .001 6.91 ± 3.891 F(4,645) = 15.675, p < .001 
 Center 60.09 ± 14.092 8.50 ± 5.382 
 Lisbon 54.41 ± 16.454 10.20 ± 4.627 
 Alentejo 54.82 ± 18.746 7.59 ± 4.299 
 Algarve 63.12 ± 10.624 4.94 ± 2.703 
Residence area 
 PUA 55.63 ± 14.879 F(2,647) = 2.094, p = .124 8.60 ± 4.871 F(2,647) = 8.450, p < .001 
 MUA 54.43 ± 16.408 7.79 ± 4.298 
 PRA 58.62 ± 14.456 6.46 ± 4.064 
Marital status 
 Married 57.24 ± 13.431 t = 3.527, p = .001 7.65 ± 4.551 t = 4.842, p ≤ .001 
 Single 51.60 ± 18.789 9.70 ± 4.917 
Employment status 
 Active 45.84 ± 11.549 t = 23.147, p ≤ .001 9.81 ± 4.739 t = 9.673, p ≤ .001 
 Inactive 66.16 ± 10.829 6.46 ± 4.065 

Notes: M = mean; SD = standard deviation; t = Student's t-test values; F = analysis of variance values.

Table 5.

Analysis of group differences in the MoCA scores while controlling for the effect of covariates and estimation of the effect sizes

Variables  Covariates  ANCOVA  Effect size 
Age  Educational level  F (2,646) = 34.098, p < .001  Medium forumla 
Educational Level  Age  F (3,645) = 117.459, p < .001  Large forumla 
Geographic Region  Age and Educational level  F (4,643) = 4.972, p = .001  Small forumla 
Residence Area  Educational level  F (2,646) = .122, p = .885  Null forumla 
Marital Status  Age and Educational level  F (1,646) = .014, p = .907  Null forumla 
Employment Status  Age and Educational level  F (1,646) = 3.469, p = .063  Null forumla 
Variables  Covariates  ANCOVA  Effect size 
Age  Educational level  F (2,646) = 34.098, p < .001  Medium forumla 
Educational Level  Age  F (3,645) = 117.459, p < .001  Large forumla 
Geographic Region  Age and Educational level  F (4,643) = 4.972, p = .001  Small forumla 
Residence Area  Educational level  F (2,646) = .122, p = .885  Null forumla 
Marital Status  Age and Educational level  F (1,646) = .014, p = .907  Null forumla 
Employment Status  Age and Educational level  F (1,646) = 3.469, p = .063  Null forumla 

Notes: F = analysis of covariance (ANCOVA) values; forumla = partial eta-squared value.

According to Cohen (1988), forumla values of 0.01, 0.06, and 0.14 are considered small, medium, and large effect sizes, respectively.

The results indicated that after controlling for the effects of covariates, only the variables age, educational level, and geographic region contributed significantly to the explanation of variance of the MoCA scores. In the subsequent analysis, we only considered variables whose effect size was medium—age: F(2, 646) = 34.098, p < .001, forumla = 0.095—or large—educational level: F(3, 645) = 117.459, p < .001, forumla = 0.353. The variable geographic region revealed a small effect size—F(4,643) = 4.972, p = .030—and, therefore, was not further examined.

Statistically significant correlations were observed between the MoCA scores and age (r = −.522, p < .01) and educational level (r = .652, p < .01). To examine the contributions of these variables and their interactions to the explanation of variance of the MoCA scores, a MLR analysis was performed using the enter method. This analysis resulted in two significant regression models. The first model—F(1, 648) = 480.093, p < .001—included only the variable educational level (β = 0.652, t = 21.911, p < .001), which significantly explained 42.5% of total variance of the MoCA scores. In the second regression model, the two variables were combined, and no evidence of multicollinearity was detected. In this model, F(2, 647) = 317.016, p < .001, both variables significantly contributed to the prediction of the MoCA scores (educational level: β = 0.524, t = 16.871, p < .001; age: β = −.293, t = −9.426, p < .001. The β weights indicated that educational level was the major contributor to the prediction of the MoCA scores, but age also contributed to the prediction. The adjusted R2 value was .49, which signifies that 49% of the variance on the MoCA scores was explained by this model.

The health variables considered in the study were as follows: family history of dementia (16% of participants had a positive family history), depressive symptoms (GDS mean = 7.34 ± 5.371, range = 0–20), and subjective memory complaints (two conditions: (a) SMC participants [mean = 5.66 ± 3.592, range = 0–18] and (b) SMC informants [mean = 4.15 ± 2.735, range = 0–11]). The results of the intercorrelations among the MoCA scores and these health variables are provided in Table 2, except for the family history of dementia, which showed no significant correlation with MoCA scores (r = .00, p < .001) or SMC informants scores (r = .095, p = .239) and a significant correlation with GDS scores (r = .092, p = .019) and SMC participants scores (r = .127, p < .01).

We observed that MoCA scores only showed a statistically significant and negative correlation with depressive symptoms and the subjective memory complaints of the participants. The influence of these health variables on MoCA performance was investigated using MLR analysis, a stepwise method. The resulting model—F(2,647) = 64.860, p < .001—only included the subjective memory complaints of the participant, which explained 9% of the total variance on the MoCA scores. Depressive symptoms did not reveal a significant contribution to the model (β = −.082, t = 1.845, p = .065).

Discussion

A reliable evaluation of an individual's cognitive performance must be based on robust normative data stratified according to the sociodemographic variables most influential on and predictive of performance. The current study is essential for adapting this instrument for a specific population. Furthermore, the study is relevant due to the widespread international use of the MoCA, the lack of international studies that analyze a wide variety of variables that may influence one's performance on this test, and the absence of studies using stratified community-based samples in Portugal to examine the influence of sociodemographic and health variables on cognitive screening instruments. The few studies available were limited by small samples within restricted regional areas and only focused on specific variables. The use of a sample stratified by different levels of sociodemographic variables and with a distribution close to that observed in the Portuguese population enhances the equivalence with the target population and the confidence of conclusions drawn.

Our results confirm that age and educational level significantly contribute to the prediction of the MoCA scores, explaining 49% of the results variance. This is considered a large effect, according Cohen (1988), and a respectable result, according to Pallant (2007). As expected, and according to previous studies of cognitive screening tests (Anderson et al., 2007; Bravo & Hébert, 1997; Gallacher et al., 1999; Langa et al., 2009; Matallana et al., 2011; Mathuranath et al., 2007; Moraes et al., 2010), our results confirm that older age and lower educational level have a significant effect on MoCA performance, increasing the likelihood of obtaining a lower total score.

The influence of other sociodemographic variables on screening tests is further conflicting in the literature. In the present study, gender, marital status, and employment status did not reveal a significant effect on the MoCA results. Regarding geographical variables, our results indicated no statistically significant differences between subjects living in the coastal and inland areas. The differences observed among residents in predominantly urban or rural areas were not significant after controlling for education. Finally, the observed differences between residents in different geographic regions showed a small magnitude after controlling for age and education. Of note, these regional subgroups were not completely matched for age and education, which may explain the results obtained.

Regarding the influence of health variables on MoCA performance, similar to a previous study (Mías et al., 2007), the results suggest no significant association between family history of dementia or memory complaints evaluated by the informant and MoCA performance. On the other hand, both depressive symptoms and subjective memory complaints of the participant presented significant and negative correlations with MoCA total scores. Moreover, these variables also showed a significant correlation between them, which is consistent with the well-documented association between these symptoms (Reid & MacLullich, 2006). Considering the results of the MLR analysis performed, only the subjective memory complaints of the participant showed a small contribution (9%) to the explanation of the variance on the MoCA scores. However, these data regarding health variables should be interpreted with caution. Since they result from the analysis of performance of cognitively healthy and non-depressed-to-mildly-depressed individuals, these findings should not be generalized to individuals with clinical conditions. Additionally, the findings about the depressive symptoms and subjective memory complaints would benefit from better operationalization of these variables. The inclusion of more specific and descriptive instruments may have shed greater light on the influence of these variables on MoCA performance. Furthermore, these findings must be complemented with studies that consider patients with cognitive impairment and patients with depression.

Another limitation of the present study was the inability to completely match all of the age subgroups in terms of education due to the higher education of the younger group. However, the observed discrepancy is, in fact, representative of the demographic profile of the country. This can be explained by the change in the school system in the last decades, namely the imposition of higher levels of obligatory education, which has had a selective impact on the younger generations. The older strata were characterized by a very low mean education. Another issue involves the classification of participants as cognitively healthy subjects. To ensure cognitive health, we established strict criteria for inclusion and exclusion in the sample, as previously explained, and these criteria were confirmed in the clinical interview and neuropsychological evaluation. Furthermore, for older participants, confirmatory information was also obtained through a general practitioner, community centre directors, and/or an informant. However, given the sample size and geographical distribution of the participants, it was not possible to perform a neurological consultation or additional diagnostic tests such as neuroimaging, which would have further ensured the normal cognitive status of participants. Finally, due to the lack of international studies analyzing the influence of sociodemographic and health variables on MoCA performance, there is no comparison for these results.

This study is a useful approach for better understanding MoCA performance in a community population. The influence of education and age on MoCA scores was clearly demonstrated, and therefore, these variables are the optimal criteria for the establishment of MoCA normative data for the Portuguese population.

Funding

This work was supported by the Fundação para a Ciência e Tecnologia [Portuguese Foundation for Science and Technology] (SFRH/BD/38019/2007) to SF; and the Fundação Calouste Gulbenkian [Calouste Gulbenkian Foundation] (Proc.74569, SDH-22 Neurociências).

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