Journal ofGemntology: PSYCHOLOGICAL SCIENCES
1999, Vol. 54B, No. 3, P155-P16O
Copyright 1999 by The Gervntohgical Society ofAmerica
Cognitive Activity in Older Persons From
a Geographically Defined Population
Robert S. Wilson,1'24 David A. Bennett,1-2 Laurel A. Beckett,1-3 Martha C. Morris,1-3 David W. Gilley,1-2-4
Julia L. Bienias,1-3 Paul A. Scherr,5 and Denis A. Evans1-2-3
'Rush Alzheimer's Disease Center and Rush Institute for Healthy Aging, Chicago, Illinois.
Department of Neurological Sciences, 3Medicine, and 4Psychology, Rush-Presbyterian-St. Luke's Medical Center, Chicago, Illinois.
5
Health Care and Aging Studies Branch, Center for Disease Control and Prevention, Atlanta, Georgia.
2
Patterns of cognitive activity, and their relation to cognitive function, were examined in a geographically defined, biracial
population ofpersons aged 65 years and older. Persons (N = 6,162) were given cognitive performance tests and interviewed
about their participation in common cognitive activities, like reading a newspaper. Overall, more frequent participation in
cognitive activities was associated with younger age, more education, higherfamily income, female gender, and White race;
participation in activities judged to be more cognitively intense was not strongly related to age, but was associated with more
education, higherfamily income, male gender, and White race. Substantial heterogeneity in activity patterns remained after
accounting for demographic factors, however. In an analysis controlling for demographic variables, level of cognitive function on performance tests was positively related to composite measures of the frequency and intensity of cognitive activity.
Longitudinal studies are needed to assess the relation of cognitive activity patterns to stability and change in cognitive function in older persons.
E
NGAGEMENT in cognitively demanding activities has
long been considered a potential modifier of cognitive
change in older persons. Cross-sectional studies suggest that cognitive activity is positively related to cognitive function
(Arbuckle, Gold, & Andres, 1986; Arbuckle, Gold, Andres,
Schwartzman, & Chaikelson, 1992; Arbuckle, Gold, Chaikelson,
& Lapidus, 1994; Christensen & Mackinnon, 1993; Cockburn &
Smith, 1991; Crabtree, Antrim, & Klenke,1990; DeCarlo, 1974;
Hultsch, Hammer, & Small, 1993; Rice & Meyer, 1986), but its
relation to age has been inconsistent. In some studies, older persons reported spending more time in cognitive activities than
younger persons (Pfeiffer & Davis, 1971; Salthouse, 1993;
Surber, Kowalski, & Pena-Paez, 1984), but other studies found
either the opposite association (Baltes & Lang, 1997; Christensen
& Mackinnon, 1993; Hultsch et al., 1993), or mixed results
(Ratner, Schell, Crimmins, Mittleman, & Baldinelli, 1987; Rice,
1986; Rice & Meyer, 1986). These inconsistencies may reflect
several issues. First, prior studies have been conducted on selected groups rather than in defined populations, possibly biasing
findings—especially because volunteering for a research study
may be considered a form of cognitive activity. Second, because
daily occupation strongly influences activity patterns (Rice,
1986), it is difficult to compare younger persons, most of whom
work or are in school, with older persons, many of whom are retired. In addition, cognitive activity has been defined and measured in a variety of ways. Studies differ, for example, in the nature and number of activities examined, how information about
activity participation was ascertained and quantified, and how
summary measures were derived.
In the present study, we sought to describe participation in
common cognitive activities among persons aged 65 years and
older, from a geographically defined, biracial community of
Chicago. An activity was considered cognitive if information
processing was central to it, and if physical demands and social
requirements were minimal. Participants were asked how often
they usually engaged in the activity, and which subform of the
activity they most frequently pursued. Summary measures of
cognitive activity were developed from this information. We
then examined the association of cognitive activity with age;
we also evaluated its relation to education, family income, gender, and race, and whether these variables could account for any
age-associated differences in cognitive activity. Finally, the association of cognitive activity with cognitive test performance
was examined in analyses that controlled for age, education,
family income, gender, and race.
METHODS
Participants
The study took place in a geographically defined community
on the south side of Chicago made up of three adjacent neighborhoods: Morgan Park, Washington Heights, and Beverly. As
part of the Chicago Health and Aging Project, from October
1993 to April 1997 all households in this community were censused, and all persons aged 65 years and older were asked to
participate in an in-home interview. The interview took an average of 85 minutes and included questions about demographic
variables, health problems, and current functioning, as well as
physical measurements and cognitive and physical performance
tests. Race was assessed with U.S. Census questions; education
with years of formal schooling; and income by asking participants to select one of ten levels of total annual family income
using the "show-card" method from the Established Populations
for Epidemiologic Studies of the Elderly (Cornoni-Huntley,
Brock, Ostfeld, Taylor, & Wallace, 1986). Interviewers were recruited from the community. They underwent an average of
three weeks of training, and were certified on interview procedures using performance-based criteria. Data were collected on
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WILSONETAL.
laptop computers, with forms programmed in Blaise (Central
Bureau of Statistics, the Netherlands), a Pascal-based data entry
program. Data quality was monitored with range and logic
checks and periodic review of selected indices for outliers.
Of 8,501 age-eligible persons identified in the census, 439
died and 249 moved before being asked to participate in the interview; of the remaining 7,813 persons, 6,162 participated in
the interview (78.9% overall; 81.4% of Blacks, 75.1% of
Whites). The mean age of participants was 75.0 years (SD =
7.2); 3,605 persons were aged 65 to 74 years; 1,869 were aged
75 to 84 years; and 688 were aged 85 years and older. The
mean educational level was 11.8 years (SD = 3.7). The participants were 60.7% female; 59.1% were Black, 40.2% were
White, and 0.2% belonged to other racial groups. The study
was approved by the Human Investigation Committee of RushPresbyterian-St. Luke's Medical Center.
Measurement of Cognitive Activity
The interview included questions about time typically spent
in seven basic activities: viewing television; listening to radio;
reading newspapers; reading magazines; reading books; playing games such as cards, checkers, crosswords, or other puzzles; and going to museums. Participants were asked to rate on
a 5-point scale how often they typically engaged in each activity as follows: (5) every day or about every day; (4) several
times a week; (3) several times a month; (2) several times a
year; (1) once a year or less. For viewing television and listening to radio, persons indicating daily activity were asked to estimate average daily hours.
For six of these basic activities (excluding "going to museum") people were questioned about participation in activity
subtypes. All persons were asked to designate the type of television program (12 types; e.g., game shows, soap operas, educational programs) and radio show (9 types; e.g., sports, news,
jazz) most frequently listened to or watched. Persons indicating
activity frequency of at least several times a year were asked to
indicate the most frequently read newspaper section (11 sections; e.g., advertisements, business pages, comics) and book
type (6 types; e.g., fiction, hobbies, religious), and to name the
three magazines most often read and the three games most often
played. Magazines were subsequently sorted into six types (e.g.,
news and opinion, informational, popular culture), and games
into six types (e.g., chance, strategy, word/information retrieval).
A panel of raters was formed to estimate the level of "cognitive intensity" involved in each basic activity (e.g., viewing television) and activity subtype (e.g., viewing soap opera on television)
on a 5-point scale, with higher numbers indicating an activity requiring effortful, demanding cognitive function. The panel consisted of 10 doctoral psychologists (5 female; 9 White, 1
Hispanic) and 20 lay persons (10 female; 10 White, 10 Black).
One person whose ratings were consistently very different from
the others, as indicated by being beyond the range of the whiskers
on standard box plots of the distributions of the ratings (Tukey,
1977), was removed from the panel. A repeated-measures
ANOVA found no differences between lay and expert raters (for
the effect of group, F[l,27] = 1.85,/? = 0.19; for the interaction
between rating and group, F[61,1647] = 1.17,/? = 0.18). The lay
group included subgroups of Black and White, male and female
raters. A repeated-measures ANOVA found no differences
among the four lay subgroups (for the effect of group, F[3,15] =
0.67, p = 0.59; for the interaction between rating and group, F
[183,915] = 0.81,/? = 0.97). Because there were no strong effects
of expertise, gender, or race, the mean rating of each item by all
29 panel members was used as a measure of the intensity of the
cognitive functioning involved in the activity.
Measurement of Cognitive Function
The population interview also included three performance
measures of cognitive function: immediate and delayed recall of
12 ideas contained in a brief, orally presented story (Albert et
al., 1991; Scherr et al., 1988); a modified form of the oral version of the Symbol Digit Modalities Test (Smith, 1984), a symbol substitution procedure which measures perceptual speed;
and the Mini-Mental State Examination (Folstein, Folstein, &
McHugh, 1975), a 30-item test of mental status that is widely
used to measure cognitive functioning in older persons. In a
principal components analysis, all tests had loadings of .79 or
more on a single factor that accounted for about 74% of the variance. Therefore, a summary measure was constructed. Scores
on each of the four tests were converted to z scores and then averaged to yield a single, global measure of cognitive function
scaled in standard units, with positive scores indicating higher
performance.
Data Analysis
Descriptive summaries of the measures of cognitive activity
and function included frequency tables, means, standard deviations, and graphical displays (box plots and stem-and-leaf displays). The relations among the cognitive activity items were
examined using pairwise correlations, item-total correlations,
and smoothed scatter plots, to determine the extent to which
summary measures could be formed that would capture substantial fractions of the variation in activity across participants.
Pairwise correlations, smoothed scatter plots, and generalized
additive models (Venables & Ripley, 1994) were used to examine the relations of the summary measures with demographic
variables, and of cognitive function with summary activity and
demographic variables. In all regression models, predictor variables were entered simultaneously; models were evaluated for
nonlinearity, interactions, and collinearity using standard diagnostic and graphic procedures.
RESULTS
The frequency of participation in the seven basic cognitive
activities is shown in Table 1. The television and museum questions yielded especially skewed distributions, with most participants indicating daily television viewing and one or fewer museum trips per year. Those indicating daily television viewing
(90.5%) estimated an average of 3.8 hours per day (SD = 2.9).
Those reporting daily radio listening (54.2%) indicated an average of 1.9 hours per day (SD = 2.9). Correlations among these
items and of each item with the adjusted item total are shown in
Table 2. Inter-item correlations were positive and modest in
size with a median correlation of. 17. Each item was positively
related with total score for the other six items. Inter-item correlations were lowest for viewing television, and highest for reading newspapers and magazines.
On the basis of these data, four measures of cognitive activity were constructed. Given evidence of shared item variance,
responses to all seven items were averaged to yield a composite
COGNITIVE ACTIVITY AND AGE
measure of cognitive activity frequency. This measure had a
mean of 3.05 (SD = 0.71), and its distribution was approximately normal (Figure 1). Responses to the three reading items
were averaged to form a reading frequency measure (M = 3.13;
SD = 1.12). Measures of television frequency (M = 7.61; SD 3.20) and radio frequency (M = 4.63; SD = 3.97) were created
by combining the activity frequency rating with estimated hours
of activity per day. This was done by adding daily hours minus
one to the frequency category, with daily hour estimates of 13
to 24 (given by 1.8% for television and 2.4% for radio) treated
as 13. Thus, a person estimating 4 hours of daily television
viewing would receive a score of 8 (i.e., [4-1] + 5).
The mean of the panel's ratings served as the cognitive intensity score for each activity. Intensity scores for the seven basic
activities are shown in Table 1; viewing television had the lowest intensity score, and reading a book had the highest. Scores
for activity subtypes ranged as follows: television, 1.4 (advertisement/shopping program) to 3.6 (educational program);
radio, 1.7 (folk music or program) to 2.8 (news); newspaper,
1.8 (advertisements) to 3.4 (business pages, crossword/other
game); magazine, 1.9 (shopping catalog) to 3.4 (news and opinion, idea-oriented); books, 2.2 (children's) to 4.2 (health/medical); game, 2.0 (chance) to 4.4 (strategy).
The cognitive intensity scores were used to form secondary
measures. First, the frequency of each basic activity was multiplied by its intensity score, and these products were averaged.
A second measure was formed by multiplying basic activity
frequency by the intensity score for the corresponding activity
subtype, and averaging the products. Both frequency-by-inten-
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sity measures of cognitive activity were highly correlated with
the composite measure of cognitive activity frequency (r > .95
for both measures), and so were not used in subsequent analyses. Third, correlations among cognitive intensity scores for the
six activity subtypes were examined. These correlations were
generally low, and in some cases negative. A composite measure of cognitive intensity was constructed by averaging scores
for three items (television, newspaper, magazine) with significant positive intercorrelations (median r = . 16, all p < .001).
This measure had a small positive correlation with the composite measure of cognitive activity frequency (r = .23, p < .001).
The association of cognitive activity with age and other demographic variables was examined with linear regression models. In an initial analysis, the composite measure of cognitive activity frequency was inversely related to age (F[l,6114] = 343.9,
p < .001), with an adjusted R2 of .05. Next, education, family income, gender, and race were added to the model (Table 3).
Activity frequency continued to be inversely correlated with
age; more frequent cognitive activity was also associated with
more years of education, higher family income, female gender,
and White race. Together, these demographic factors accounted
for over one fourth of the variance in the composite measure of
cognitive activity frequency (adjusted R2 = .27).
These multiple regression analyses were repeated for the
measures of reading, television, and radio frequency. The re-
Table 1. Frequency of Basic Cognitive Activities in the Population
and Means and Standard Deviations of Cognitive Intensity Ratings
Activity Frequency
Activity
Daily
Few/
Wk
View television
Listen radio
Read newspaper
Read magazine
Read book
Play game
Visit museum
90.5
54.2
59.5
13.0
26.9
13.2
0.1
5.0
12.5
11.5
20.4
11.3
9.3
0.1
Activity Intensity
Few/
Mo
Few/
Yr.
0,1/
Yr.
1.5
8.4
9.6
24.3
10.9
13.0
1.0
0.4
3.4
2.2
8.3
10.8
10.0
14.9
2.3
21.1
16.9
33.6
39.8
54.1
83.4
Mean St. Dev.
2.2
2.4
2.9
2.9
3.4
3.0
3.3
1.1
1.1
0.6
0.7
0.9
0.7
0.8
Cognitive Activity Frequency
Figure 1. Distribution of composite measure of cognitive activity frequency
in population. Higher scores indicate more frequent activity.
Table 2. Correlations of Basic Cognitive Activity Items with Other
Items and Adjusted Item Totals
TV
View television —
Listen radio
Read newspaper
Read magazine
Read book
Play game
Visit museum
Radio Newsp. Mag. Book Game Mus.
.08
—
.19
.21
—
.11
.20
.41
—
.07
.13
.18
.28
—
.09
.10
.25
.21
.11
—
.02*
.12
.18
.22
.17
.15
—
Tota.
.18
.25
.44
.46
.27
.26
.27
a
Refers to adjusted item total, which is the sum of scores on the remaining
six items.
V>.10;allotherp<.001.
Table 3. Summary of Regression of Composite Measure of Cognitive
Activity Frequency on Demographic Variables
Model Term
Age
Education
Family Income
Gender"
Race"
Parameter Estimate*
Standard Error
-0.017
0.060
0.043
-0.167
-0.181
0.001
0.002
0.004
0.018
0.020
*parameter estimates are unstandardized; p < .001 for each
Male= 1, Female = 0
"Black = 1 , White = 0
a
WILSONETAL.
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suits for the reading measure were similar to those for the composite frequency measure. Reading activity was inversely related to age and positively related to education and family income; women and White persons reported reading more than
men and Black persons did, respectively (adjusted R2 = .22).
The regression models accounted for relatively little variance in
television viewing and radio listening (adjusted R2 = .03 for
both models). Both activities were inversely related to age.
Television viewing was inversely related to education and family income; radio listening was positively related to those variables. There were no gender differences in either activity. Black
persons reported more television viewing and less radio listening than did White persons.
Because the scaling of the television and radio measures may
not have been optimal, alternative scores for these activities
were computed by converting data on frequency and daily
hours to z scores, which were averaged. The results of regression analyses using these alternate measures of television viewing and radio listening essentially were unchanged from the primary analyses.
The regression analyses were repeated for the composite
measure of cognitive activity intensity. With age alone in the
model, there was a significant (/? < .001) but very small (adjusted R2 = .004) positive association between age and intensity.
With allfivedemographic variables in the model (Table 4), substantially more variance was explained (adjusted R2 = .23); age
still showed a small, positive association with intensity, and
higher intensity was also associated with more education,
higher family income, male gender, and White race. In a secondary analysis, a composite intensity measure based on all six
items produced similar results, except that the age effect was
reduced to a trend.
Family income data were unavailable for 14% of the population. To see if exclusion of these persons affected results, each
of the regression analyses was repeated without family income.
The results of these analyses were comparable to those obtained
in primary analyses, except that women reported somewhat less
television viewing than men (F[ 1,6084] = 4.4, p = .04).
Because previous studies have shown a relation between cognitive activity and cognitive function, we examined the correlations of the activity measures with the global performance-based
measure of cognitive function. Each activity measure was positively related to cognitive function (all/? < .001), with composite
frequency (r = .54) and reading (r = .47) showing higher correlations, and composite intensity (r = .23), radio (r = .20), and
television (r = .04) showing lower correlations. The smoothed
function for the association (general additive model) between
the composite frequency and cognitive function measures, with
95% pointwise confidence intervals for the relationship, is
shown in Figure 2. To further examine the association, a series
of linear regression analyses was performed. In an initial analysis, cognitive function was positively related to composite frequency and composite intensity measures (both/? < .001; adjusted R2 = .17). In another analysis, cognitive function was
related to age, education, family income, gender, and race (p <
.001 for each term, adjusted R2 = .34). In a third analysis, the
cognitive function measure was regressed on the two activity,
and five demographic variables. Both activity measures were
significant (for frequency, F[l,4502] = 170.2,/? < .001; for intensity, F[ 1,4502] = 17.0, p < .001) as was each demographic
variable (all/? < .001; adjusted R2 = .37). When the analysis was
repeated without the family income term, thereby increasing the
number of persons available for analysis, results were unchanged (all/? < .001; adjusted R2 = .36). To see if the results depended on a subset of persons with very poor cognitive function,
the analysis was repeated excluding persons with global cognitive scores at or below the tenth percentile. Comparable results
were obtained (all/? < .001; adjusted R2 of .35).
DISCUSSION
In this biracial, urban population of persons aged 65 years
and older, participation in common cognitive activities varied
substantially. Overall, more frequent participation in cognitive
activities was associated with younger age, more education,
higher family income, female gender, and White race. Together,
these demographic variables accounted for about one fourth of
the variance in a composite measure of activity frequency, with
age alone accounting for about 5%. The tendency to engage in
activity subtypes judged to be relatively more cognitively intense or demanding was associated with older age, more education, higher family income, male gender, and White race. Age
accounted for less than 1% of the variance in this composite in-
Table 4. Summary of Regression of Composite Measure of Cognitive
Activity Intensity on Demographic Variables.
Model Term
Age
Education
Family Income
Gender3
Race"
Parameter Estimate*
Standard Error
0.003
0.022
0.016
0.087
-0.186
0.001
0.002
0.002
0.010
0.011
*parameter estimates are unstandardized; p < 001 for each
Male = l; Female = 0
"Black = 1 ; White = 0
a
Cognitive Activity Frequency
Figure 2. Smoothed association between composite measure of cognitive
activity frequency and global, performance-based measure of cognitive function derived from generalized additive model. Higher scores indicate more frequent activity and better performance. The dashed lines are approximate 95%
confidence intervals.
COGNITIVE ACTIVITY AND AGE
tensity measure, however, and was not significantly correlated
with a secondary measure of intensity. In an analysis that controlled for demographic variables, level of cognitive function on
performance tests was positively related to composite measures
of the frequency and intensity of cognitive activity.
Prior research on the association of age with cognitive activity has yielded mixed results, as noted earlier. However, because most of the studies have included both younger and older
persons, age ranges have differed substantially from the range
in the present study (65 to 108 years), making it difficult to
comparefindings.Another important consideration is that previous studies have been conducted on selected groups, making
it difficult to estimate securely the association of activity with
age and other demographic variables. In contrast, participants
in the present study were identified in a census of a geographically defined area of Chicago; all persons aged 65 years and
older were asked to participate and over 6,000 (78.9% of those
eligible) did so, thereby substantially reducing the likelihood of
selection bias and increasing the likelihood that a broad spectrum of cognitive activity patterns, demographic variables, and
cognitive function would be represented.
Assessment of cognitive activity poses several challenges.
Most activities are cognitive to some degree; how best to quantify that degree and characterize individual differences, especially among persons from diverse cultural backgrounds, socioeconomic levels, and birth cohorts is unclear. Our approach
was to define cognitive activities as those in which information
processing was central, and to focus on activities believed to
make relatively minimal physical or social demands and to be
relatively common in this population. Because economic factors can limit opportunities to participate in these activities, and
because income was related to frequency of participation, a
term for income was included in analyses.
The best way to summarize activity information is uncertain,
so several measures were formed. The construct validity of
these measures can be indirectly assessed by examining their
correlations with education and the performance-based measure of cognitive function. The composite measures of frequency and intensity had positive correlations with these criteria of small-to-moderate size. Among the specific measures of
activity frequency, reading and, to a lesser extent, radio listening were positively related to cognitive function and education.
By contrast, the television frequency measure had a negligible
correlation with cognitive function, and a negative correlation
with education, suggesting that television viewing may involve
relatively low levels of cognitive activity.
Another difficult issue in measuring cognitive activity is
whether to weight frequency of participation in a given activity by
some measure of how cognitively demanding or intense that activity is (Arbuckle et al., 1986; Christensen & Mackinnon, 1993).
In our study, measures based on the product of frequency and intensity had near-unity correlations with measures based on frequency alone; Arbuckle and associates (1994) reported a similar
result with a more diverse set of activities. On the other hand, a
composite measure of cognitive intensity, which reflected a tendency to opt for more or less demanding subforms of several activities, did contribute to the prediction of cognitive function independently of frequency information.
Knowledge about the relation of cognitive activity with gender or race is limited. In this urban population, women reported
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more overall cognitive activity and reading, equivalent television viewing and radio listening, and engaged in somewhat less
cognitively demanding activities, when compared with men.
White persons reported more overall cognitive activity, reading,
and radio listening, less television viewing, and engaged in
somewhat more cognitively demanding activities, when compared with Black persons. In future studies, it will be important
to determine whether these subgroup differences are observed
in other populations, with other measures of cognitive activity,
and whether they contribute to different patterns of aging.
Among persons in this population, individual differences in
level of cognitive function were related to both the frequency
and intensity of cognitive activity. Other studies have also reported positive correlations between measures of cognitive activity and cognitive function, but the basis of this association is
uncertain. There is substantial evidence that in children (Ceci &
Williams, 1997; Gottfried, 1984) and in some middle-aged
adults (Kohn & Schooler, 1978; Miller, Slomczynski, & Kohn,
1985; Owen, 1966) exposure to complex, intellectually demanding environments in the home, school, or workplace is associated with enhanced cognitive functioning. Little is known,
however, about the relation of cognitive activity to change in
cognitive function in older persons. More longitudinal studies
are needed to understand whether and how current and lifetime
levels of cognitive activity are related to person-specific change
in cognitive function in older people.
ACKNOWLEDGMENTS
This research was supported by grants (AG11101, AG101G1) from the
National Institute on Aging. The authors thank the residents of Morgan Park,
Washington Heights, and Beverly for their cooperation and support; Michelle R.
Bos and her staff for data collection; and Todd Beck for statistical programming.
Address corespondence to Robert S. Wilson, Rush Institute for Healthy
Aging, 1645 West Jackson Blvd., Suite 675, Chicago, IL 60612.
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Received June 11,1998
Accepted December 7, 1998
Just published by GSA and now available—
Full-Color Aging:
Facts, Goals, and Recommendations for America's Diverse Elders
Edited by Dr. Toni Miles.
Contributors: Jacob Siegel, Yung-Ping Chen, Marie-Florence Shadlen and Eric Larson,
Toni Miles, Robert John, and Peggye Dilworth-Anderson and Linda Burton.
From the Preface:
In this revised edition of GSA's Minority Elders, authors have been invited to describe the
new demographic, medical, and societal realities of the 21 st century age wave as they
relate to America's minority elders.
New features of this volume include a chapter on dementia and a discussion of the health
care delivery system. This collection of articles is thought provoking as well as informative.
Professionals—teachers, clinicians, and researchers—should find this volume to be a
handy reference for many of the issues confronting gerontology and geriatrics.
Regardless of the reader's professional background, this text holds surprises.
For members, $10. For nonmembers, $15.
To purchase a copy, contact Charles Clary at GSA, 1030 15th St., NW, Suite 250, Washington,
DC 20005-1503, (202) 842-1275, ext. 114, E-mail: [email protected].