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Activity levels and cognitive functioning in an elderly community sample.


There is a widespread belief, particularly among elderly people, that activity prevents cognitive decline in old age and protects against the onset of dementia. This view is reflected in the philosophy of many organizations, such as the University of the Third Age [1], which claim that mental activity is crucial in preventing cognitive impairment. The importance of an active lifestyle has also received support within the scientific community [2-6]. A recent editorial in the British Medical Journal [7] stated that `stimulating mental activity is worthwhile' (p. 952) in protecting against dementia and called for further experimental investigation of the issue.

The present study investigated the role of activity on cognitive performance in a large community sample of elderly subjects. The primary aim was to determine whether differences in cognitive performance could be accounted for by activity, particularly when the influence of other variables, such as sex, sensory functioning [8], health [9], education [7] and disability were taken into account. These variables have been found previously to be predictive of cognitive performance. A secondary aim was to examine whether age continued to be predictive of cognitive performance once account had been taken of activity, health, education, sex, disability and sensory functioning. This allowed investigation of the hypothesis that activity and other variables account for age-associated changes in cognitive functioning. This hypothesis predicts that once the effects of activity are accounted for, no further age effects should be present. A third aim was to examine the hypothesis that activity was more influential in older than younger subjects. Christensen and Mackinnon [10] and Hultsch et al. [6] noted that maintaining high levels of activity may be of greater importance in very elderly subjects than in younger elderly people. Furthermore, because earlier findings suggested that activity levels were not independent of education, and that activity may offset to some degree education disadvantage [10, 11], the interactions between activity and education were also examined.


The sample: The subjects were a sample of elderly persons living in the community in the Australian city of Canberra and adjacent town of Queanbeyan Elderly people were sampled from the electoral roll which is a register of eligible voters. Registration on the electoral roll is compulsory for all Australian citizens aged 18 or more. The sample was drawn at random within three age strata to give an equal achieved sample of men and women. For each sex, there were three age strata (70-74 years, 75-79 and more than or equal to 80 years), with each stratum sampled to give an achieved sample which was proportional to the number of individuals in the population at that age group.

The persons sampled were sent a letter inviting participation in the survey and then approached at home by trained interviewers. The purposes of the study and what was asked of the participant were explained orally. If a person was unable to give informed consent owing to disability, the principal caring relative was asked. If an interview was granted, the subject was asked at the end to nominate a close relative or friend who knew him/her well and would be able to describe his/her present circumstances. An interview was then sought from the informant nominated. Thirty-one per cent of those approached refused to participate. This refusal rate is similar to those recently obtained in other community samples. Sixty-five per cent of refusers were women, but the age range of the refusers did not differ from that of the participants. The sample used in the present paper consisted of 858 subjects aged 70-89 years of age who completed data on the self-activity scales, the Mini-Mental State Examination (MMSE) [12] and sensory functioning. Of these, 664 subjects had informant data available. Subjects were divided into four age groups, 70-74, 75-79, 80-84 and 85-89 years.

General procedure: Subjects were interviewed in their own homes by trained interviewers. Information necessary for the diagnosis of dementia and depression was collected, as well as information on social background, personality, social support, self-reported cognitive function and the use of services. Prevalence rates for depression and dementia have been reported elsewhere, as have data on health, neuroticism and cognitive functioning [13-18].

Measures of inactivity: Both informants and subjects were asked about how often `these days' the subject read a newspaper, engaged in physical activity (such as sport, walking, heavy gardening, cleaning), was involved in interests and hobbies, spent time sitting around without doing very much (e.g. resting, dozing, watching the TV without caring what the programme was), spent time in planned activities, and had a daily nap. Each of these activities was rated on a scale with from 2 to 4 points. For example, subjects were asked `How often do you read a newspaper, book or magazine these days?' (0 = every day, 1 = most days, 2 = once a week, 3 = less than once a week). The six items were combined to form a scale. Five items had 4 points and one item had 2 points, making 16 the maximum score for the scale. Scores were pro-rated if up to three of the six items were completed. If fewer than three items were completed, the scale was listed as missing. Informants' reports were summed in the same manner to give scores from 0 to 16, with higher scores indicating greater inactivity. The correlation between informant and subject total scores on the activity measures was 0.74 (n = 664), showing strong agreement. Cronbach alpha for the self-report scale was 0.64 (n = 841) and for the informant scale was 0.69 (n = 603).

Because the items on the scale measured a variety of physical, social and mental activities, the scales were subjected to principal components analysis to confirm that a general activity factor was measured. Two factors with eigenvalues greater than 1 were revealed on the self-report scale. All six items loaded on the first factor (eigenvalue of 2.27) with 38% of variance accounted for. Unrotated loadings for the items were as follows: read newspaper 0.44; physical activities 0.64; interests and hobbies 0.62; time sitting around 0.78, planned activities 0.77; and have a nap 0.28. Two items loaded on the second factor (eigenvalue 1.02) accounting for a further 17% of variance. These were `time sitting around' and `have a nap'. The informant scale produced a similar pattern of factors with factor 1 accounting for 42% of the variance. These factor analyses confirmed that the scales measured a general activity factor and that self-reported and informant-reported scales had a very similar factor structure.

Measures of health: Two measures of health were used. These were summary measures found useful in an earlier study of this population [14]. In the present study, these measures controlled for individuals having multiple health problems and current multiple symptoms. More detailed analysis of effects of specific health problems on cognitive performance are reported elsewhere [14]. The first health measure asked subjects to note whether they had ever had any of 28 medical conditions such as stroke, heart attack, severe psychiatric disorder (such as schizophrenia), diabetes, cataract, thyroid dysfunction, and so on. The number of medical conditions reported by the subjects was aggregated to form the medical condition scale. For the second measure, subjects noted whether they suffered currently from any of 21 symptoms, such as cramps, breathing difficulties, indigestion, and headaches. The number of complaints was summed to obtain the second health score which was labelled current health problems [14].

Measures of sensory functioning: Subjects were asked about their eyesight and hearing on ten items. Two of the items required overall ratings of corrected vision and hearing [`Would you say your eyesight (with glasses) is generally good (score 4), fair (3), poor (2) or blind (1)?'; `Would you say your hearing (with a hearing aid) is generally good (3), fair (2) or poor (1)?']. Other items referred to specific behaviours. The questions were: `Under the best conditions, can you see to read newspapers?; letters and mail?; labels on medicines?; street signs or bus numbers?'. `Are you able to hear what somebody is saying to you when there is background noise from radio and television?; when others are talking?; out in the street?; travelling?'. These items were scored I for `no', 2 for `yes, with difficulty' and 3 for `yes'. In addition, the interviewer rated the subject's hearing and vision at the end of the interview. `How well did the subject seem to hear you?' (4 = very well, 3 = quite well, 2 = somewhat impaired, 4=profoundly deaf). `How well was the subject able to see the papers given to him/her?' (4=very well, 3=quite well, 2 = somewhat impaired (could not see photographs), I = interviewer had to read aloud or omit questions requiring sight). All 12 items were summed to provide a score ranging from 1 to 26. There were 194 (22%) subjects who achieved the maximum score. A further 28% scored 24 or 25 points. Higher scores indicated better sensory functioning.

Measures of Activities of Daily Living (ADL): Each informant and subject was asked about the subject's capacity to get about when travelling, to walk about 400 metres without pain or difficulty, to get in and out of bed, to get dressed, bathed and showered, to get in and out of an armchair, to dress completely without difficulty, to take care of feet and toe nails and to get to the toilet. The eight items were combined to form a scale. Each item was rated on a scale with from 3 to 5 points. The items were summed separately for subjects and informants to give scores ranging from 0 to 22, with high scores indicating disability. The correlation between the informant and subject for the eight items was 0.88 indicating excellent agreement. The scale has face and content validity. Moreover, scores on the ADL scale have been found to be related to informant ratings of ability to live independently, and, if not able, whether this was due to physical disability or, to a lesser extent, behavioural disability [17].

Education: The total number of years of schooling and post-school training was used to form the education measure.

Measures of cognitive functioning: The cognitive battery consisted of tests of memory, crystallized and fluid intelligence, and speed, as well as the MMSE. Details of the tests are reported elsewhere [13]. Briefly, the cognitive battery consisted of 14 tasks including standard tests. They were: (1) the National Adult Reading Test (NART) [19] which relies on the reading of words that are not pronounced phonetically; (2) The Symbol Letter Modalities Test (SLMT) which is a modified version of the (oral) Symbol Digit Modalities Test [20]; (3) Choice and simple reaction time, each measured over 20 trials, with the choice reaction time task requiring subjects to respond to one of two lights; (4) Vocabulary, which consisted of three items from the Wechsler Adult Intelligence Scale-Revised (WAIS-R) [21]; (5) Similarities, which consisted of three items from the WAIS-R; (6) Information, which consisted of four items identifying historical figures; (7) Cube drawing, where subjects were required to produce a copy of a cube from a drawn two-dimensional figure; (8) Memory for a figure, which consisted of Item 1 (flags) from the Visual Reproduction subtest of the Wechsler Memory Scale [22]; (9) Recall of words, a task requiring the recall of three words learned to criterion; (10) Address recall, which required subjects to recall a name and address learned to criterion 1 to 2 minutes earlier; (11) Face recognition; which was an intentional face recognition task, similar to that used in the Rivermead Behavioural Memory Test [23]; (12) Word recognition, which was an incidental memory task in which subjects were asked to answer 13 questions (including 1 buffer item), each of which contained key words, and which were later recognized from 24 items (12 targets and 12 distractors); (13) Verbal Fluency, where subjects were asked to say aloud as many animals as they could within 30 seconds, and [14] MMSE, a brief screening test for dementia. Because of the high correlations among outcome measures, data from the cognitive tests (excluding the MMSE) were subjected to a principal components analysis with varimax rotation. Three factors which emerged with eigenvalues greater than 1.00 were retained in the factor solution. From their factor loadings, these factors were labelled Crystallized Intelligence, Fluid Intelligence and Memory. These factor scores were used in the analyses thereby reducing the number of outcome measures. Because not all subjects completed all tests in the cognitive battery, only 703 subjects contributed to the factor scores. There were 858 subjects who provided data from the MMSE.


Differences in education, activity, health, ADL and cognitive functioning as a function of age: Table I shows means and standard deviations in age groups for sensory functioning, medical conditions, current health problems, education, self- and informant-reported measures of activities of daily living. The statistical significance of mean differences of these variables was examined using ANOVA. Sensory functioning declined significantly across age groups, while education and health did not. Both subject-and informant-reported measures of ADL were higher in the older age groups, indicating that disability, as measured by the ADL scales, was greater in older age groups.


The data for the self-reported inactivity and informant-reported inactivity scales were examined separately using multivariate analysis of variance (see Table I). Overall, both self-reported and informant-reported inactivity scores were higher in older age groups indicating lower activity levels in the older age groups [Wilks' lambda F(18, 2502)=2.98 for self-reported activity, and Wilks' lambda F(18, 1788) = 2.12 for informant-reported activity]. With the exception of `read newspaper', all items on the self-report inactivity scale indicated lower activity levels in the older age groups. For the informant scale, all items except `little involvement in interests and hobbies' and `daily nap taking' indicated lower activity levels in older groups.

Cognitive test scores are also shown in Table I. Differences among age groups for crystallized intelligence, fluid intelligence and memory factors were examined using multivariate analysis of variance. With the exception of crystallized intelligence, older groups had lower factor scores than did younger groups. ANOVA indicated that older age groups had lower MMSE scores than did younger age groups.

Correlations among activity, age and cognitive performance: Correlations between activity measures and cognitive performance are shown in Table II. Both self-and informant-reported activity measures correlated significantly, albeit weakly, with the three factors and the MMSE. The correlations ranged from-0.32 to -0.10. The strength of the correlations between the cognitive factors and activity levels was assessed using the [T.sub.2] statistic described by Steiger [24]. These tests examined whether activity was associated more strongly with specific cognitive factors. Correlations may have been expected to be weaker between activity and crystallized intelligence since crystallized intelligence shows the least age-associated decline. Thus, tests were undertaken to compare the magnitude of the correlation between activity and crystallized intelligence with the correlation between activity and each of the cognitive test scores. Taking the subject reports of activity, the correlation with crystallized intelligence was not significantly different from the correlation with fluid intelligence scores [[T.sub.2](704) = 1.75, p > 0.05] or the correlation with Memory [[T.sub.2](704) = 0. 19, p > 0.05], but it was significantly weaker than the correlation of activity with MMSE [[T.sub.2](855) = 2.71, p < 0.01]. For the informant reports of activity, the correlation with crystallized intelligence was again not significantly different from the correlation with memory [[T.sub.2](559) = 0.68, p > 0.05] but was significantly weaker than both the correlation of activity with fluid intelligence [[T.sub.2](561) = 3.87 =p < 0.01] and with the MMSE [[T.sub.2](661) = 2.82, p < 0.01].
Table II. Correlation coefficients (r) between activity and
performance on cognitive factors and the MMSE and age
                   Activity measure
                   Self report         Informant report
                   r    No.[dagger]     r     No.[dagger]
  intelligence    -0.17(*)    707          -0.10(*)   564
  intelligence    -0.26(*)    707          -0.32(*)   564
Memory            -0.16(*)    707          -0.14(*)   562
MMSE              -0.27(*)    858          -0.22(*)   664
Age                0.21(*)    858           0.17(*)   664

(*) p < 0.05.
[dagger]Number in group.

Hierarchical regression analysis: Hierarchical regression analysis was employed to test three issues: (i) whether activity made a significant contribution to variance once account had been taken of sex, sensory functioning, health and education, (ii) whether age accounted for further variance after controlling for the effects of activity and the other contextual variables; and (iii) whether activity influenced cognitive performance to a greater extent in older elderly groups. Separate regressions were undertaken for self-reported and informant-reported activity measures. Four dependent variables (crystallized intelligence, fluid intelligence, memory and the MMSE) were examined for each of these two data sets. Thus, to take account of the probable influence of contextual variables, the following variables were entered on steps 1 to 6 of the analyses: sex, sensory functioning, ADL, past medical conditions, current health problems and education. Activity was entered on step 7, followed by age (step 8) and then by the interaction variables of activity-by-education (step 9), age-by-activity (step 10) and age-by-education (step 11). The interaction terms were created using the products of the relevant variables. In this analysis, product terms moderate the effects of variables, and thus, for example, if the interaction of a variable and age adds significantly to the regression equation this implies that the effect of the predictor variable varies as a function of age [25]. The results of the regression analysis using self-reported activity measures are shown in Table III.


Self-reported and informant-reported activity measures: The findings indicated that activity contributed significantly, albeit modestly, to the amount of variance accounted for in the four cognitive measures beyond that contributed by sensory functioning, sex, ADL, health and education. Age continued to be associated with performance levels on fluid intelligence, memory and MMSE performance beyond that contributed by activity and the other contextual variables. This finding indicates that age-associated differences in cognitive performance cannot be accounted for entirely by these contextual variables.

Age-by-activity interaction effects were present for crystallized and fluid intelligence but not for memory or for the MMSE. For crystallized intelligence, the interaction effect indicated that low levels of activity were associated with lower levels of crystallized intelligence at younger rather than older ages. For fluid intelligence, the interaction was in the opposite direction, where low activity was associated with lower levels of fluid intelligence but only in older elderly subjects.

There were several other findings of interest. Across all three factors and the MMSE, education, sex and sensory functioning were found to be strongly and fairly consistently associated with cognitive performance. ADL was found to be predictive of fluid intelligence scores but not of the other test scores, probably because tests loading on fluid intelligence required speed and motor co-ordination (for example SLMT, choice and simple reaction time).

The findings from the regression analysis using the informant-reported activity scales were very similar to those for the self-reported scale. There was, in addition, a significant education-by-activity effect for the memory factor. This indicated that high activity was associated with higher performance levels in less well educated subjects. Activity made little difference to performance in highly educated subjects.

Exclusion of low Mini-Mental State scorers, DSM-III-R Dementia and Depression: A small number of low-scoring subjects may have been responsible for many of the significant relationships between activity and cognitive performance. This possibility was examined in a further analysis by excluding all individuals scoring below 24 on the MMSE, a level regarded as predictive of probable dementia. Data on the MMSE were available for 673 subjects. Overall, the results were very similar to those found for the total sample using both self and informant scales. The 23/24 cut-off on the MMSE may identify moderate to severe cases but is less likely to identify mild, early onset or possible dementia cases. Further analysis of subjects with MMSE scores of 27 and above was conducted. This analysis produced results that were very similar to those for the subsample with MMSE scores above the 23/24 cut-off. A final analysis was undertaken excluding subjects with DSM-III-R Depression and Dementia as diagnosed using the Canberra Interview for the Elderly [15, 16]. Similar findings were found, supporting the view that the results could not be accounted for by subjects with dementia or depression.

Activity as a `proxy' for cognitive competence: One of the major problems with cross-sectional analyses of this kind is that activity may simply serve as a proxy measure of cognitive competence. In this respect, it would be reassuring to find that activity continued to influence cognitive performance once the effects of `cognitive competence' had been accounted for. Chronological age might reflect changes in the nervous system intrinsically linked to `biological ageing' and therefore emerge as an indicator of cognitive competence. This issue can be investigated by introducing the effect of age in the first step of a regression analysis followed by examination of the independent contribution of other variables. These sets of analyses with age entered first, for self-and informant-reported activity, were performed. For self-reported activity measures, variables in addition to those of age which accounted for significant variance were identical to those found in the original set of analyses. Most importantly, activity continued to contribute independently to variability in cognitive performance for both self-reported and informant-reported analyses.


This study investigated the relationship between activity and performance on a cognitive test battery in a sample of elderly people living in the community. The primary aim was to determine whether age differences in cognitive performance could be accounted for by activity, particularly when the influence of other variables, such as sex, sensory functioning, health, education and disability were taken into account. We expected that levels of inactivity would increase with age while levels of cognitive performance would decrease, that low levels of activity would be associated with poor fluid and memory performance, and that accounting for the effects of activity would reduce age differences. In addition, we examined whether activity was of more importance in reducing age differences for older age groups than for younger groups or those with poor education compared with those with more extensive education.

Our study differs in important respects from earlier ones: (i) Reports of activity levels were taken from both the subject and an informant who knew the subject well. No earlier studies have attempted to substantiate self-reported activity measures. (ii) We used a representative sample. The most relevant earlier studies [e.g. 4, 6] examined volunteers. (iii) The range of outcome measures was greater than in previous studies. We included measures of fluid intelligence, crystallized intelligence and memory. Earlier studies used mainly verbal memory tasks. By including a wide range of measures it was possible to examine whether activity correlated more strongly with certain types of cognitive functioning. It might be expected that the association between activity level and cognitive ageing would be more pronounced for fluid and memory tasks than for crystallized, since the former tasks are known to have the greatest age-associated decline [6]. (iv) Finally, we attempted to control for sensory functioning and physical disability, variables that have previously not been examined in studies of this kind but which may not be independent of activity levels.

The effect of activity on cognitive test performance: We found that both self- and informant-rated activity levels declined significantly with increasing age and that activity levels influenced the level of cognitive performance independently of sensory functioning, activities of daily living, education, and health. Importantly, age continued to be associated with variance of the cognitive measures once the influence of activity (and the other contextual variables) had been taken into account. This latter finding refutes the hypothesis that cognitive changes in ageing can be accounted for entirely by the contextual variables activity, health, sensory functioning and ADL. When control ford mental competence was attempted, by entering age first and by excluding low MMSE scorers, the influence of activity was still present consistently. The fact that activity accounted for additional variance, over and above that of sensory functioning and ADL, suggests that activity influenced cognitive performance independently of disability and sensory dysfunction. This then lowers the probability that better performance on speeded tasks (such as those used in the measurement of fluid intelligence) was due to improved psychomotor performance rather than to enhanced cognitive competence.

Correlational analysis established that the contribution of self-reported activity to cognitive test performance was no greater for fluid intelligence or memory than for crystallized intelligence, although it was greater for the MMSE. Once account was taken of contextual variables in the regression analysis, however, it appeared that activity contributed somewhat greater variance to fluid intelligence scores than to the MMSE (see Table III). On the basis of the present data it would be premature to conclude that activity influenced particular cognitive test scores more than others.

The present study found some support for the view that activity levels were of greater importance in old than in young subjects. For fluid intelligence, significant interactions were found between age and activity. This interaction suggested that activity `offsets' the effects of age for fluid intelligence measures. For crystallized intelligence, activity was of greater importance as a predictor of lower scores in younger than in older elderly subjects. There are various possible explanations for the effect. Low activity in younger subjects may be a sensitive indicator of dementia, which is known to affect crystallized intelligence. Low activity at older ages may be a less sensitive indicator of dementia because factors other than dementia, such as health and motivation, may influence activity levels at more advanced ages. Alternatively, activity may be associated with improved cognitive functioning but only above a certain level of participation. Since activity levels decline with age, activity levels at older ages may be below the threshold above which they influence cognitive processes.

The findings for fluid intelligence and activity were consistent with earlier work. Christensen and Mackinnon [10] found significant interactions between age and physical activity on fluid intelligence tasks, suggesting that activity level `compensated' for loss of cognitive abilities on fluid intelligence tasks. However, our findings for crystallized intelligence were not consistent with an earlier study on activity levels using largely verbal tasks [6]. However, differences in measurement and sample composition were likely to be responsible for these differences.

Despite the significant relationships we found between activity and cognitive performance, one of x the clearest findings from the study was that the influence of activity on cognitive performance was modest. Across all factors and for both self and informant report, the variance accounted for by activity ranged from 0 to 3%. This was lower than that for sex (0-4%), sensory functioning (range 0-7%), ADL (range 0-6%), education (range 1-18%) and for age (range 04%), but not for health which failed to contribute significantly to any of the factors. The magnitude of variance explained by activity is very similar to that noted by Hutsch et al. [6]. Nevertheless, despite the modest amount of variance explained, the fact that both self-rated and informant-rated activity consistently predicted significant variance for all factors points to its importance as a correlate of cognitive performance in old age.

Importance of other contextual variables in predicting performance: Recent reports have emphasized the importance of sex, sensory functioning, education and health factors in predicting cognitive performance of elderly people. In particular, visual and auditory acuity have been reported to become more important as age advances and account for a large proportion of variance. It has been proposed that age differences in intelligence, including speeded tasks, may be mediated by vision and hearing [8]. While the present study found a consistent relationship between sensory measures and outcome, the relationship was much weaker than that reported by Lindenberger and Baltes [8]. We found simple correlations between sensory functioning and crystallized intelligence to be 0.09, fluid intelligence to be 0.14, Memory to be 0.14 and MMSE to be 0.15. Lindenberger and Baltes [8] used objective measures of hearing (uncorrected) and vision, had a smaller sample of volunteers, used a slightly greater age range (70-103 years), had a different sampling distribution, and used a different cognitive test battery. These differences may account for discrepancies between studies, although the strength of correlations found in Lindenberger and Baltes' study needs confirmation from other work.

Threats to validity of findings: Two potential problems with the activity scale were that it was limited in the range of activities covered and, like other self-reports of activity, it may be subject to bias [26]. In an epidemiological study, the number of questions asked is necessarily restricted. To overcome this limitation, each question was framed so as to cover a broad range of activities, without presuming which specific `organized' activities were engaged in, and to include activities that were almost universal (e.g. reading newspaper). Methods of assessing activity used in other studies, such as summing items on an activity list, may themselves be biased because they allow those who engage in a variety of organized activities to score highly. However, despite their limitations, there were a number of indications that our activity scales had validity. (i) The results from the self-report measure were substantiated by an informant. Although, this does not entirely counter the criticism that such shared perceptions are nevertheless biased [see 27], it has been established that informants can be reasonably competent when assessing aspects of behaviour, such as cognitive decline, in their elderly relatives [18]. (ii) The factor analysis identified the presence of one major factor and the factor structure for the informant and self report were the same. It is significant that the items, although measuring seemingly different characteristics, did form a scale, suggesting there may be an underlying single construct.

Potential problems with the health scales were that they aggregated equally weighted disparate medical conditions and current symptoms. More comprehensive scales measuring health factors known to influence cognitive functioning may be seen as more useful to control for health factors in the present analysis. However, the present scales were designed to control for individuals with multiple health complaints. Moreover, previous analysis of the same population using a variety of health measures failed to find relationships with cognitive functioning [14]. The failure to find strong relationships between cognitive functioning and health has been reported for other elderly populations [9]. Consequently, the use of these scales in this population sample to control for multiple health problems seems justified.

Because the study is cross-sectional, the activity relationships may be due to cohort effects. Although this explanation cannot be ruled out entirely, there is no evidence or explanation for a more sedentary life-style being present in the older cohorts. Also, because the data are cross-sectional, it is difficult to draw conclusions about the direction or nature of the relationship between cognitive performance and activity. As noted by others, for example Salthouse et al. [9], high levels of cognitive function may be a prerequisite for certain sorts of experiences. In these cases, rather than activity improving mental functioning, mental functioning may influence the type of activity participated in and thus reflect cognitive competence. The direction of causation between activity levels and cognitive performance can better be clarified in longitudinal studies, although the direction of the relationship, even there, can be difficult to determine [29].

The data from the present study are from the first wave of a longitudinal study and the relationship of activity with cognitive decline will be examined after wave 2 data are collected. Nevertheless, the present study confirms that it is `prudent to recommend to elderly people that stimulating mental activity is worthwhile' [7]. However, the study findings might suggest the following caveats: (i) activity levels will only make a small amount of difference; (ii) other factors such as sensory and motor functioning are also important; and (iii) despite the contribution of all these factors, cognitive performance will tend to get worse with advancing age.


[1.] Swindell R. A model for successful ageing. Ageing Soc 1993;13:245-66. [2.] Gribbin K, Schaie WK, Parham IA. Complexity of life style and maintenance of intellectual abilities J Soc Issues 1980;36:47-61. [3.] Paulson JS, Weisston CC, Heaton RK. The neuropsychology of aging. Curr Opin Psych 1994;7:347-53. [4.] Arbuckle TY, Gold DP, Andres D. Cognitive functioning of older people in relation to social and personality variables. Psychol Aging 1986;1:55-62. [5.] Arbuckle TY, Gold DP, Andres D, Schwartzman A, Chaikelson J. The role of psychosocial context, age, and intelligence in memory performance of older men. Psychol Aging 1992;7:25-36. [6.] Hultsch DF, Hammer M, Small BJ. Age differences in cognitive performance in later life: Relationships to sel-freported health and activity life style. J Gerontol Psychol Sci 1993;48:1-11. [7.] Orrell M, Sahakian B. Education and dementia: research evidence supports the concept of `use it or lose it', Br Med J1995;310:951-2. [8.] Lindenberger U, Baltes P. Sensory functioning and intelligence in old age: a strong connection. Psychol Aging 1994;9:339-55. [9.] Salthouse TA, Kausler DH, Saults JS. Age, self-assessed health status and cognition. J Gerontol Psychol Sci 1990;45:156-60. [10.] Christensen H, Mackinnon A. The association between mental, social and physical activity and cognitive performance in young and old subjects. Age Ageing 1993;22:175-82. [11.] Huppert FA. Age-related changes in memory, learning and remembering information. In: Boller F, Grafman, J, eds, Handbook of neuropsychology. Amsterdam: Elsevier, 1988. [12.] Folstein MF, Folstein SE, McHugh PR. `Mini-Mental State': a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:179-98. [13.] Christensen H, Mackinnon AJ, Jorm AF, Henderson AS, Scott LR, Korten AK. Age differences and interindividual cognition in community dwelling elderly. Psychol Aging 1994;9:381-90. [14.] Christensen H, Jorm AF, Henderson AS, Mackinnon AJ, Korten AK, Scott LR. The relationship between health and cognitive functioning in a sample of elderly people in the community. Age Ageing 1994;23:204-12. [15.] Henderson AS, Jorm AF, Mackinnon A et al. The prevalence of depressive disorders and the distribution of depressive symptoms in later life: a survey using Draft ICD-10 and DSM-III-R. Psychol Med 1993;23:719-29. [16.] Henderson AS, Jorm AF, Mackinnon A et al. A survey of dementia in the Canberra population: experience with ICD-10 and DSM-III-R criteria. Psychol Med 1994;24:473-82. [17.] Jorm AF, Henderson S, Scott R, Mackinnon AJ, Korten AK, Christensen H. The disabled elderly living in the community: care received from family and formal services. Med J Aust 1993;158:383-7. [18.] Jorm AF, Christensen H, Henderson AS, Koten AE Mackinnon AJ, Scott LR. Complaints of cognitive decline in the elderly: a comparison of reports by subjects and informants in a community survey. Psychol Med 1994;24:365-74. [19.] Nelson HE. National Adult Reading Test (NART). Berkshire, England: NFER-Nelson, 1982. [20.] Smith A. The Symbol Digit Modalities Test. Los Angeles: Western Psychological Services, 1973. [21.] Wechsler D. Wechsler Adult Intelligence Scale-Revised. New York: Psychological Corporation, 1981. [22.] Wechsler D. A standardized memory scale for clinical use. J Psychol 1945;19:87-95. [23.] Wilson B, Cockburn J, Baddeley A. The Rivermead Behavioural Memory Test. Reading, Berks.: Thames Valley Test Company, 1985. [24.] Steiger JH. Test for comparing elements of a correlation matrix. Psychol Bull 1980;87:245-51. [25.] Fisher GA. Problems in the use and interpretation of product variables. In: Long JS, ed. Common problems/proper solutions. Beverly Hills: Sage, 1988. [26.] Bunce DJ, Warr PB, Cochrane T. Blocks in choice responding as a function of age and physical fitness. Psychol Aging 1993;8:26-33. [27.] Ross M. Relation of implicit theories to the construction of personal histories. Psychol Rev 1989;96:341-57. [28.] Jorm AF, Mackinnon AJ, Christensen H, Henderson S, Scott R, Korten A. Cognitive functioning and neuroticism in an elderly community sample. Pers Individ Differ 1993;15:721-3. [29.] Farrell AD. Structural equation modeling with longitudinal data: Strategies for examining group differences and reciprocal relationships. J Consult Clin Psychol 1994;62:477-87.

Authors' addresses H. Christensen, A. Korten, A. F. Jorm, A. S. Henderson, R. Scott National Health and Medical Research Council, Social Psychiatry Research Unit, The Australian National University, Canberra 0200, Australia

A. J. Mackinnon Mental Health Research Institute, Parkville, Victoria, Australia

Received in revised form 22 June 1995
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Author:Christensen, Helen; Korten, Ailsa; Jorm, A.F.; Henderson, A.S.; Scott, Ruth; Mackinnon, A.J.
Publication:Age and Ageing
Date:Jan 1, 1996
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