Assessment of Cognitively Stimulating Activity in a Spanish Population

Abstract

Theoretical models of active ageing and cognitive reserve emphasize the importance of leading an active life to delay age-related cognitive deterioration and maintain good levels of well-being and personal satisfaction in the elderly. The objective of this research was to construct a scale to measure cognitively stimulating activities (CSA) in the Spanish language. The sample consisted of a total of 453 older persons. The scale was constructed from a list of 28 items and validated using structural equation models. The scale obtained showed a negative correlation with age and a positive correlation with education and physical activity. Using hierarchical regression models, CSAs were found to have a significant effect on attention when controlling for the effect of age and education. Likewise, a significant interaction between age and CSA was found on the measure of episodic memory. The validated CSA scale will enable the relationships between changes in cognitive functions and stimulating activities to be studied.

Keywords

ageing leisure activities lifestyle neuropsychology measurement

Life expectancy in Western countries has increased in recent years. This has meant that governments have become increasingly concerned about the social and health consequences involved in having an ageing population. In 2015, there were 901 million people older than 60 years, and this figure is expected to rise to 2.1 billion in 2050 (United Nations, 2015). Ensuring that these older adults maintain their physical and cognitive skills and capabilities in the best condition possible has both individual and societal benefits. From the point of view of the individual, improving the quality of life of people in this age-group will prevent (or delay) the possible appearance of problems of dependency and achieve better quality of life during those years. From a societal point of view, keeping older adults healthy and active will help reduce the budgets allocated to dealing with their health problems.

Various studies have shown that age-related decline (physical and mental) can be delayed or moderated depending on a series of factors. One of these is active participation in leisure activities (LA). Older adults who engage in LA show a better quality of life and better cognitive functioning than those with lower levels of activity (Fratiglioni, Paillard-Borg, & Winblad, 2004; Hertzog, Kramer, Wilson, & Lindenberger, 2008). It has been found that the risk of developing Alzheimer’s disease is less in older adults who regularly carry out physical activities (PA) than those who lead a sedentary lifestyle (Hertzog et al., 2008; Raji et al., 2016; Schooler & Mulatu, 2001).

Several theories have sought to explain these results. Active ageing theories have related the well-being and personal satisfaction of older people with greater participation in LA (Hertzog et al., 2008; Jopp & Hertzog, 2010; Lee, Lan, & Yen, 2011). More active people will seek out more complex situations that will yield greater satisfaction and allow them to stimulate their cognitive functions (Schooler & Mulatu, 2001). In the same way, the theory of “use it or lose it” (Bielak, Anstey, Christensen, & Windsor, 2012; Hertzog, 2009) considers that older people who lead an active life will always present higher levels of cognitive functioning over time (preserved differentiation). Likewise, people with more active lifestyles will have lower levels of change (decline) than the less active ones (differential preservation).

The cognitive reserve (CR) theory also considers that the experiences or events that individuals are subjected to may compensate for the effect of age-related brain damage (Reed et al., 2011; Stern, 2009). Studies based on this theory have found a relationship between lifestyle and the onset of dementia (Fernández-Mayoralas et al., 2015; Fratiglioni et al., 2004; Sobral, Pestana, & Paúl, 2015; Valenzuela & Sachdev, 2006; Verghese et al., 2003; Wilson, Barnes, & Bennett, 2003).

Assessments of cognitively stimulating activities (CSA) have been carried out using self-reports (Salthouse, Berish, & Miles, 2002; Schinka et al., 2005; Wilson et al., 2003). Reliability measures were based on the alpha coefficient (varying between .60 and .88) and/or the test–retest alpha (varying between .73 and .79). The three studies mentioned were validated by associating the CSA measure with measures of neuropsychological functioning (Schinka et al., 2005), or by associating the CSA measures with measures of cognitive functioning using linear regression and after controlling for factors such as education (Wilson et al., 2003), or gender and age (Salthouse et al., 2002). The results obtained correlated positively with education, level of occupation, and measures of cognitive functioning, and negatively with age.

There is at present no measurement in Spanish that enables us to reliably measure and isolate CSA; there are only measures of CR (León, García-García, & Roldán-Tapia, 2014; Rami et al., 2011), which include various factors (e.g., level of occupation, education, hobbies, social life, daily activities, etc.) but do not allow us to identify the effects of CSA in isolation. Moreover, these tests were carried out with very small samples. A scale was recently developed to measure LA (Martínez-Rodríguez, Iraurgi, & Gómez-Marroquin, 2016) but was not designed specifically to measure CSA. Their items are associated more with personal satisfaction than with cognitive stimulation.

Since there is at present no scale for measuring this construct, the objective of this study was to develop a scale to measure CSAs in the Spanish context. Having an independent measure of CSA available in Spanish will make it possible to follow up with longitudinal studies of activities of this kind, as well as to discover their effects, either independently or in their interactions with other variables, such as age, education, or PA. This interaction has been regarded as a basic condition for determining the effectiveness of CSA in cognitive functioning (Salthouse et al., 2002). Our initial hypothesis, therefore, was that CSAs would be associated with different cognitive functions. It was expected that, as the number of these activities increased, so there would be a corresponding increase in cognitive functions, which would be considered as evidence of the validity of the scale, particularly if the cognitive changes were observed to interact with variables such as age.

Method

Participants

The sample was made up of 453 people mostly of older age living in the province of Seville, Spain (320 females), with a mean age of 66.34 years (SD = 6.95, range = 48-90 years), and an average number of 10.35 years in formal education (SD = 4.97, range = 0-25 years). In all, 91.8% (416) of the sample had occupations at skill levels 1 and 2 (unskilled, manual workers, agricultural workers, etc.) using the CNIO-11 classification (Instituto Nacional de Estadística, 2012). These people were enrolled in the Experience Classroom at the University of Seville, which is an activity that lasts 4 years and allows older people to gain access to education and cultural activities (seminars and lectures). Subjects were excluded if they fulfilled any of the following criteria: (1) previous history of neuropathology; (2) previous hospitalization due to psychopathological disorders (e.g., schizophrenia, depression, etc.); (3) previous history of abnormal psychomotor development; (4) a history of drug or alcohol abuse; (5) taking psychotropic medication that affects attention and concentration or causes sleepiness; (6) mother tongue was not Spanish. The mean score of subjects taking the Mini-Examen Cognoscitivo, the Spanish version of the Mini-Mental State Examination (MMSE; Lobo, Ezquerra, Burgada, Sala, & Seva, 1979) was 28.02 (SD = 2.07) and the mean score on the Spanish short version of the Geriatric Depression Scale developed by Martínez de la Iglesia et al. (2002) was 3.19 (SD = 2.78, range = 0-15). An attempt was made to include subjects from both urban and rural environments. Subjects participating in the study provided informed consent. Authorization to perform the study was given by the management of the Experience Classroom and the Ethics Committee of the University of Seville.

Instruments

Questionnaire about Sociodemographic and Health Variables

Individuals were asked about their age, what jobs they had had during their lives, level of education (the number of years of formal education received was recorded), what medical conditions or illnesses they had, at the time or previously, PA that they carried out and how often, what medication they were taking, and so on. PA was measured using a self-report Likert-type scale that asked them to indicate how often (1 = less than a couple of times a year, 2 = a couple of times a year, 3 = a couple of times a month, 4 = a couple of times a week, and 5 = every day) they performed 9 PA (walking, cycling, swimming, keep-fit exercises, jogging, yoga and/or Pilates, dance classes, meditation, and looking after plants/lawn mowing).

Cognitively Stimulating Activities Scale

A Likert-type scale was constructed, initially made up of 28 items, which asked how often CSAs of varying degrees of difficulty were carried out (see Table 1). The items were selected after considering the three scales that had previously been validated in an English-speaking environment (Salthouse et al., 2002; Schinka et al., 2005; Wilson et al., 2003). The scale was graded from 1 to 5 depending on how often the activity in question was performed (1 = less than a couple of times a year, 2 = a couple of times a year, 3 = a couple of times a month, 4 = a couple of times a week, 5 = every day). Table 1 shows items selected. A Spanish translation of the items is provided in the supplementary material (available in the online version of the article).

Table 1.

List of Cognitive Stimulation Activities.

Item	Activity
1	I watch television and/or listen to the radio.
2	I watch talk shows on TV or listen to them on radio.
3	I use computers.
4	I read books, newspapers, poetry, etc.
5	I write letters, diaries, etc.
6	I write poems, novels, etc.
7	I have a hobby (crossword puzzles, Sudoku, etc.)
8	I play games with other people (chess, cards, dominoes, etc.)
9	I listen to music.
10	I belong to a club/society or take part in some social activity.
11	I go to the movies, theater, exhibitions.
12	I take the occasional pleasure trip (tourism, hiking, . . .)
13	I spend part of my time on my hobbies.
14	I go out for a drink with my friends.
15	I visit friends or relatives.
16	I look after a (dependent) relative.
17	I try to learn new things.
18	I paint pictures.
19	I try to repair things when they stop working.
20	I take classes (not including sports).
21	I give classes (include any classes).
22	I take part in discussions on any topic that crops up (with my family or my friends, etc.)
23	I can handle my financial affairs unaided.
24	I cook at home.
25	I look for and try out new recipes.
26	I drive my car or ride my motorbike.
27	I clean my house.
28	I am learning to play a musical instrument or I already have one and play it.

Verbal Selective Reminding Test

Form 1 of the Spanish version of the verbal selective reminding test was applied (Campo & Morales, 2004; Morales et al., 2010). Following Buschke’s procedure (1973), subjects were presented with a list of 12 unrelated words in 6 selective reminding trials. Their total recall (RECALL), long-term recall (LTR), consistent recall (CLTR), and delayed recall (DELAY) scores were recorded. LTR is the number of words that had already been recalled in two consecutive trials. CLTR is the number of words that had already been recalled in two consecutive trials and could still be recalled. DELAY is the number of words recalled 30 minutes after completing the six trials. The characteristics and psychometric properties of this test can be found in Campo and Morales (2004).

Stroop Effect Test

This test consists of performing three tasks on three different cards: reading words, naming colors, and a final interference task (Golden, 1978). The first of the cards involved reading the words of the colors “red,” “blue,” and “green” printed in black and randomly displayed in five columns of 20 words each; the subject was instructed to “read each column aloud as quickly as possible.” The total number of words read in 45 seconds was recorded and any subject who read the 100 words within the time limit returned to the beginning of the card. On the second card, naming colors, there were, once again, five columns with 20 “XXXX” in red, green, and blue ink, and the task involved naming the color of each element in the columns as quickly as possible in 45 seconds, as before; the number of correctly named colors was recorded as the STROOP2 score. Last, the third card, the so-called interference task, presented the same words as on the first card, but this time printed in a different color from the color named (e.g., the word “red” was printed in blue ink) and the subject was asked to name the color of the words in the columns rather than what the word said. Correct responses were recorded as the STROOP3 score. Because of the nature of the task, this card was useful for observing some components of executive function, such as inhibitory control, since the subject had to suppress the prepotent response of reading the word and change it to the name of the color of the ink in which the words were written.

Digit Symbol-Coding Test

The digit symbol-coding test is a subtest of the Wechsler Adult Intelligence Scale, WAIS-III (Wechsler, 1981), and comprises two tasks designed to measure working memory capacity and its manipulation. In the first test, the subject was asked to repeat—immediately and in the same order—a set of numbers read aloud by the researcher at the rate of one per second, in blocks of two trials. The first block started with two numbers and then increased by one each time one of the trials in a block was successfully completed; the number of correct trials was recorded in the variable (FSPAN). In the second task, designed to test the capacity to manipulate information, the same process was performed, but the subject was asked to repeat the numbers in the reverse order to the one in which they were presented, recording the correct trials in the variable (BSPAN).

Mini-Examen Cognoscitivo

This is the Spanish version of the MMSE (Lobo et al., 1979). The total score on the scale was used and a score of 24 was considered the cutoff point (Llamas-Velasco, Llorente-Ayuso, Contador, & Bermejo-Pareja, 2015).

Escala Abreviada de Depresión

This is the Spanish version of the Geriatric Depression Scale (Yesavage et al., 1982). The total score on the scale was recorded (Martínez de la Iglesia et al., 2002). A score of more than 5 was proposed as the cutoff for considering the possible existence of depression. Table 2 shows the descriptive statistics of the variables measured.

Table 2.

Descriptive Statistics for Neuropsychological and Other Variables.

Variable	M	SD	Min	Max
RECALL: Total recall	41.01	10.21	9	65
LTR: Long-term recall	26.47	13.82	0	62
CLTR: Consistent long-term recall	19.32	12.69	0	62
DELAY: Delay recall	5.76	2.81	0	12
GDS: Geriatric Depression Scale	3.19	2.78	0	15
MEC: Mini-Examen Cognoscitivo (Spanish version of MMSE)	28.02	2.07	18	30
FSPAN: Digit forward correct answers	7.62	1.77	4	15
BSPAN: Digit backward correct answers	4.97	1.84	1	12
STROOP2: Number of colors named correct	96.34	21.30	10	200
STROOP3: Number of correct answers in the interference task	61.23	13.85	16	108
PA: Physical activity	18.73	4.55	1	33
Education	10.35	4.97	0	25
Age	66.34	6.95	48	90

Note: SD = standard deviation; Min = minimum; Max = maximum; MMSE = Mini-Mental State Examination.

Procedure

The subjects were evaluated on an individual basis in one of the laboratories at the Department of Experimental Psychology. Trained psychologists explained that the objective of the research was to perform a study on memory and the subjects were invited to take part. Once the acceptance form had been signed, the six trials associated with the Verbal Selective Reminding Test were administered. The subjects then completed the sociodemographic questionnaire and the remaining tests. After 30 minutes, they were asked again to repeat as many words as they could remember from the initial list read to them half an hour earlier (delayed memory task, DELAY measure).

Data Analysis

The R program (v. 3.2.3) was used for statistical analysis (R Development Core Team, 2008). The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s sphericity test were used to determine the adequacy of the correlation matrix for performing factor analysis. Mardia’s test was applied to check the assumptions of multivariate normality. The description of the measures, calculation of the alpha coefficient, Horn’s parallel analysis and Velicer’s minimum average partial (MAP) to determine the number of factors, and exploratory factor analysis (EFA) were performed using the principal function of the psych library (v. 1.6.3; Revelle, 2008). Confirmatory factor analysis (CFA) was carried out using the cfa function of the lavaan library (v. 0.5-20; Rosseel, 2012). The diagonally weighted least squares (WLSMV) estimator proposed for ordinal variables was employed. The criteria for considering the goodness of fit of the model were as follows: a χ²/gl ratio < 2 (excellent), χ²/gl < 3 (good), good fit for the Tucker–Lewis index (TLI) > 0.9, comparative fit index (CFI) > 0.9, incremental fit index (IFI) > 0.9, and root mean square error of approximation (RMSEA) ≤ 0.05.

Results

Partition of the Sample and Items Selected

For the cross-validation study, the sample was divided into two sets using the sample function of the R program, with the objective of using the first as the training set for calibrating the test (N = 231) and the second as the test set (N = 222). The training set was used to identify the most discriminating items and to carry out EFA. The test set was used to validate the scale by means of CFA.

Of the 28 initial items, those that had low correlations with the total test score (r < .35) and a lower discrimination index (<1.23) were eliminated, leaving items 3, 5, 9, 11, 13, 14, 17, 19, 20, 22, 23, and 25. Alpha coefficient was .79, 95% confidence interval (CI) = [0.75, 0.83]. Both parallel analysis and the MAP criterion indicated that a single factor should be extracted.

Principal component analysis was carried out using the training sample. The KMO test of sampling adequacy and Bartlett’s test of sphericity indicated that EFA was appropriate: KMO index = .84, χ²(45, N = 231) = 541.863, p < .001. Mardia’s test rejected the hypothesis of multivariate normality of the data by finding a highly skewed distribution (skew = 678.52, p < .001). Velicer’s MAP criterion and parallel analysis indicated that a single component should be extracted for both scales. Principal component analysis explained 31% of variance. Item 25 correlated less than 0.35 with the general factor. This item was removed from the scale.

Confirmatory Factor Analysis

CFA of the 11 remaining items in each of the samples was carried out using the diagonally weighted least squares estimator, with mean and variance adjustment (WLSMV) suggested for ordinal data (Flora & Curran, 2004). In the test sample results, all the statistics showed a good fit: χ2(44, N = 222) = 55.966, p = .061, χ2/gl = 55.966/44 = 1.34, CFI = 0.99, TLI = 0.99, IFI = 0.99, RMSEA = 0.04, 90%CI [0.00, 0.06], p = .73. All items showed correlations greater than .40. Table 3 shows the descriptive measures of each of the items and shows the standardized parameters of the EFA and CFA models. Table 4 shows the polychoric correlations of the items.

Table 3.

Descriptive Statistics of Selected Items and Parameter Estimates (EFA and CFA).

Item	Activity	Mean	SD	R	alphaW	EFA	CFA
3	I use computers	2.79	1.82	0.65	0.77	0.62	0.67
5	I write letters, diaries, etc.	1.84	1.41	0.47	0.78	0.44	0.49
9	I listen to music	4.00	1.28	0.55	0.78	0.55	0.53
11	I go to the movies, theater, exhibitions	2.58	0.99	0.60	0.77	0.65	0.62
13	I spend part of my time on my hobbies	4.18	1.28	0.53	0.78	0.51	0.50
14	I go out for a drink with my friends	2.95	1.19	0.54	0.78	0.56	0.49
17	I try to learn new things	4.28	1.26	0.70	0.76	0.73	0.84
19	I try to repair things when they stop working	3.40	1.46	0.49	0.78	0.47	0.47
20	I take classes (not including sports)	3.38	1.6	0.64	0.76	0.65	0.69
22	I take part in discussions on any topic that crops up . . .	3.83	1.30	0.55	0.77	0.57	0.55
23	I can handle my financial affairs unaided	3.60	1.37	0.55	0.77	0.54	0.55

Note: SD = standard deviation; alphaW = index of reliability resulting from deletion of the item; EFA = exploratory factor analysis; CFA = confirmatory factor analysis.

Table 4.

Polychoric Correlation Matrix: Lower Triangular Matrix Training Sample and Upper Triangular Matrix Testing Sample.

	ACE03	ACE05	ACE09	ACE11	ACE13	ACE14	ACE17	ACE19	ACE20	ACE22	ACE23
ACE03	1.00	0.38	0.34	0.45	0.26	0.23	0.61	0.35	0.63	0.37	0.41
ACE05	0.41	1.00	0.17	0.26	0.31	0.19	0.37	0.24	0.38	0.26	0.25
ACE09	0.35	0.28	1.00	0.37	0.31	0.18	0.50	0.29	0.42	0.48	0.32
ACE11	0.31	0.28	0.35	1.00	0.18	0.44	0.39	0.11	0.50	0.31	0.45
ACE13	0.36	0.37	0.41	0.14	1.00	0.21	0.42	0.25	0.28	0.24	0.16
ACE14	0.22	0.13	0.36	0.48	0.26	1.00	0.30	0.11	0.36	0.29	0.23
ACE17	0.61	0.42	0.40	0.46	0.49	0.30	1.00	0.51	0.70	0.51	0.50
ACE19	0.22	0.14	0.17	0.30	0.18	0.19	0.36	1.00	0.29	0.34	0.43
ACE20	0.52	0.37	0.28	0.40	0.28	0.29	0.65	0.37	1.00	0.48	0.43
ACE22	0.34	0.21	0.32	0.44	0.32	0.28	0.45	0.28	0.25	1.00	0.25
ACE23	0.46	0.21	0.16	0.31	0.14	0.21	0.46	0.41	0.41	0.29	1.00

Correlational Measures

There was a positive correlation between the total score for the CSA scale and education (r = .38, p < .001), age (r = −.48, p < .001), and PA (r = .35, p < .001). No significant differences between men and women were found on the CSA scale: t(451) = 0.72, p = .47. On the other hand, significant differences were found between those with low occupational status (M = 35.64) and those with high occupational status (M = 40.38): t(451) = −3.13, p = .002.

Neuropsychological Measures

An EFA of neuropsychological measures was carried out. Both parallel analysis and Velicer’s MAP test indicated that two factors should be extracted. The first factor (Cog1) was related to measures of episodic memory (RECALL, LTR, CLTR, and DELAY) and the second factor (Cog2) to measures of attention and executive function (FSPAN, BSPAN, STROOP2, and STROOP3). All the measures associated with the episodic memory factor correlated more than .85 with it. The measures of attention and executive function showed correlations higher than .70 with the factor. The percentage of variance for the two factors was 78%. Hierarchical regressions were run with both factors, entering CSA (Model 1), as well as age (Model 2) and education (Model 3). Model 4 included the interaction between CSA and age. The results are shown in Table 5 and Figure 1.

Table 5.

Hierarchical Regression Results for Measures of Cog1 (Episodic Memory) and Cog2 (Attention).

	Dependent variable
	Cog1				Cog2
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
CSA	0.024*** (0.006)	0.006 (0.006)	−0.001 (0.006)	−0.003 (0.006)	0.053*** (0.005)	0.040*** (0.006)	0.029*** (0.006)	0.029*** (0.006)
Age		−0.047*** (0.007)	−0.043*** (0.007)	0.037*** (0.009)		−0.034*** (0.007)	−0.029*** (0.006)	0.055*** (0.009)
Education			0.036*** (0.010)	−0.040*** (0.007)			0.054*** (0.009)	−0.029*** (0.007)
CSA × Age				0.002*** (0.001)				0.0003 (0.001)
Constant	−0.798*** (0.196)	2.894*** (0.591)	2.524*** (0.591)	0.055 (0.047)	−1.737*** (0.181)	0.988* (0.556)	0.422 (0.541)	0.008 (0.044)
Observations	453	453	453	453	453	453	453	453
R ²	0.037	0.122	0.149	0.163	0.178	0.224	0.286	0.286
Adjusted R²	0.035	0.118	0.143	0.156	0.176	0.220	0.281	0.280

Note: Variables were centered in Model 4. CSA = cognitively stimulating activities.

p < .05. **p < .01. ***p < .001.

Figure 1.

Relationships between age and episodic memory factor by three different levels of cognitively stimulating activities (CSA).

It can be observed that the effect of CSA on episodic memory disappeared when the education and age variables were introduced. However, when the term interaction between CSA and age was entered, the model changed significantly. In the case of attention, the effects of CSA were not modified when the age and education variables were added. Entering the effect of the interaction between age and CSA did not improve the model.

Discussion

This study achieved the initial objective of constructing a scale to measure CSA in Spanish-speaking older adults. The test obtained presents adequate psychometric properties. The proposed factor structure is valid. This is the first time a CSA scale has been validated with structural equation modeling in Spain. As was found in previous studies (Salthouse et al., 2002; Wilson et al., 2003), CSA correlated negatively with age and positively with education and occupation (Valenzuela & Sachdev, 2006). Although the constructed scale does not include a large number of items, the correlations were similar to those obtained by Wilson et al. (2003) and Salthouse et al. (2002). Furthermore, the scale was composed of items belonging to different dimensions, such as the social dimension, or of high-level cognitive activities that have been included in other scales.

The results obtained by correlating the CSA scale with the neuropsychological variables do not completely coincide with those obtained in the three validation studies. The neuropsychological measures used in this study are comparable to those used in Wilson et al. (2003) and Schinka et al. (2005). Less comparable are the measures used by Salthouse et al. (2002) because they did not report any attention measure, and the episodic memory measures were the average of the variables instead of the score in the factor. Wilson et al. (2003) found no relationship between CSA and episodic memory, which is similar to the result found in our case (see Model 3 in Table 5 for this variable). These authors, however, did not include a term for the interaction between CSA and age. Introducing interaction meant a change in the effect of CSA on episodic memory, in the sense that it had a moderating effect. The study by Schinka et al. (2005) used measures of episodic memory that correlated with CSA, but did not perform hierarchical regression. The interaction between stimulating activities and age implies that engaging in stimulating activities can compensate for the effect of age on decline in episodic memory. Although age-related memory decline does not disappear completely, the effect is reduced by carrying out stimulating activities in old-old people. In our study, we found that while young-old people (between 50 and 65 years) did not benefit from CSA in old-old people (older than 70 years) the advantages were significant. The performance in episodic memory was higher in those with high values in CSA. A possible explanation is the positive effect that CSAs have on CR. Reed et al. (2011) found that activities of this type were strongly associated with CR, to a greater extent even than education. More follow-up longitudinal studies showing the relationship between degree of participation in such activities and cognitive decline are needed to confirm this hypothesis.

With regard to attention, Wilson et al. (2003) did not find that CSA had any effect on this either. While Schinka et al. (2005) found that CSA correlated negatively with time taken to perform the Trail Making Test B, they did not, as was the case with episodic memory, control for variables such as age and education. Our study found a difference between the effect of CSA on episodic memory and the effect on attention. If we compare two people of different ages, differences in memory are greater in those who are less active. However, these differences do not exist when we compare attention, implying that attention levels are higher as more CSAs are performed, controlling for age and level of education. A possible explanation for these results would involve considering different types of CR, as suggested by Reed et al. (2011). While CSA does succeed in slowing down the effect of age on episodic memory, the same cannot be said with respect to the decline of attentional factors. To put it simply, only those people with high levels of CSA will display higher attentional levels.

In our opinion, the CSA scale constructed offers several advantages. First, it has only 11 items, which makes it easier to collect data. Since this is a short version, it means that little time needs to be devoted to this test. Furthermore, the items finally included have been shown to be related to the preservation of cognitive functions. It has been found, for example, that the decrease in the frequency of day-to-day computer use can be an indicator associated with mild cognitive impairment (Kaye et al., 2014).

A second advantage of the scale is that it includes items that involve a social dimension, which has been considered an important element in the evaluation of LA (Adams, Leibbrandt, & Moon, 2011). Vance and Wright (2009) regard social interaction as a source of neurogenesis with a protective effect against the cognitive decline that can be expected with age. Even so, it is difficult to separate the social dimension from cognitive activity, as Schinka et al. (2005) point out. Likewise, the scale includes CSA-related items linked to the individual’s motivation for seeking novel stimulation (“I try to repair things when they stop working”). This degree of “curiosity” has also been studied as a protective factor against age-related cognitive decline (Düzel, van Praag, & Sendtner, 2016).

Another advantage of this scale is that it shows correlations with measures such as PA and education that have been linked to CR (León et al., 2014; Rami et al., 2011) and so can be used in future research as indicators of this construct. Wilson et al. (2003) pointed out that education was an indicator that was associated with CR, but it was heavily influenced by the socioeconomic level of the individual. For them, CSA is an indicator of CR independent of socioeconomic status. Nonetheless, there is a need for future validation studies in other populations to confirm the results obtained here.

However, it is also worth pointing out some limitations of this study. First, known stimulating activities such as playing a musical instrument were discarded because they had very low frequency in the study population and did not meet the statistical criteria. Second, the individual differences in a particular activity have not been taken into account. An activity like writing poetry can be relatively easy for a person accustomed to it and very difficult for someone who is not used to it. That is to say, the same activity can imply a different level of effort for each person. Finally, it is possible that the list of stimulant activities was not exhaustive and the older people performed activities not included in the test.

In conclusion, the main contribution of this work is the elaboration and validation of a scale to measure the stimulating activities of cognition in Spanish. The main difference between this scale and others is that the various activities are not just included. Items should have good psychometric properties. Likewise, it may be considered that our study provides some evidence for the “use it or lose it” hypothesis with regard to the effect of ageing on cognitive function. However, this assertion must be taken with a degree of caution, since this is a cross-sectional study and needs to be completed with results obtained from future studies of a longitudinal nature. As has been demonstrated in the literature (Salthouse et al., 2002), only by monitoring individuals over long periods of time can conclusive evidence be provided about the effect of CSA on cognitive function. It is therefore necessary to have valid measures available, such as the one developed in this study.

Supplemental Material

Tabla_1b – Supplemental material for Assessment of Cognitively Stimulating Activity in a Spanish Population

Supplemental material, Tabla_1b for Assessment of Cognitively Stimulating Activity in a Spanish Population by Manuel Morales Ortiz and Aaron Fernández in Assessment

Footnotes

Acknowledgements

We would like to thank two anonymous reviewers for suggestions to improve the article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material

The online supplementary material is available at .

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