Copyright 1998 by
The Cerontological Society of America
The Cerontologist
Vol. 38, No. 5, 556-568
The goals of this article are (a) to describe the daily life of the very old in terms of
frequency, duration, variety, and social and physical contexts of activities, and (b) to
examine the effects of background variables (e.g., age, sex, residential and marital status,
income, and education) on late life activity engagement. A representative sample of 516
adults aged 70-105 was interviewed about their activities using the Yesterday Interview. In
contrast to most research on activity engagement, this measurement approach allows for
assessment of both the type and context of activities engaged in during the day preceding
the interview. The results indicated high frequencies of obligatory activities but also
showed substantial time spent in discretionary activities, with television viewing occupying
most of the participants' leisure time. Most activities were done alone and at home. In
bivariate and multiple regression analyses, age and residential status had the strongest
association with activity frequency, duration, and variety; the oldest-old and those residing
in long-term care facilities had lower levels of activity engagement. Results are discussed
in terms of their relevance for successful aging.
Key Words: Successful aging, Oldest-old, Long-term care
Daily Life in Very Old Age: Everyday
Activities as Expression of Successful Living
Ann L. Horgas, PhD, RN,1
Hans-Ulrich Wilms, PhD,2
and Margret M. Baltes, PhD3
It is surprising that despite the wealth of knowledge
about aging and elderly adults, relatively little is
known about the daily life of older adults (Altergott,
1988; Herzog, Kahn, Morgan, Jackson, & Antonucci,
1989; Moss & Lawton, 1982; Verbrugge, GruberBaldini,
& Fozard, 1996). This is particularly true for
those older than 70 years of age. Most of the knowledge
that we do have about elders' everyday activities
stems from clinical geriatrics. The focus of that
body of work has been on assessing difficulties in acAnn
Horgas was supported by a postdoctoral National Research Service
Award, funded by the Agency for Health Care Policy and Research
(Grant #1 F32 H00077-01), during the writing of this article. The present
study is part of the Berlin Aging Study (BASE), which has been conducted
by the interdisciplinary working group "Aging and Societal Development"
(ACE) in cooperation with institutes and research units of
the Free University Berlin, the Humboldt University Berlin, and the Max
Planck Institute of Human Development in Berlin, Cermany. Aging and
Societal Development was constituted in 1987 by the former Academy
of Sciences of Berlin (West) and has been continued since 1994 by the
Berlin-Brandenburg Academy of Sciences (BBAW). BASE was financially
supported from 1989-1991 by the German Ministry of Research and
Technology (NR. TA 011 and 13 TA 011/A); since 1992 financial support
has been received from the German Ministry of Family, Seniors,
Women and Youth. Primary responsibility for BASE is shared by P. B.
Baltes, K. U. Mayer (Max Planck Institute), H. Helmchen (Free University)
and E. Steinhagen-Thiessen (Humboldt University Berlin). A central
project coordinator, M. Neher (Max Planck Institute) joins this steering
committee. Research on everyday activities in BASE was the responsibility
of M. M. Baltes (Free University of Berlin).
'Address correspondence to Ann L. Horgas, PhD, RN, Institute of Gerontology,
Wayne State University, 87 E. Ferry, 226 Knapp Building, Detroit,
Ml, 48202. E-mail: horgas@iog.wayne.edu.
'Department of Psychiatry Outpatient Clinic, University of Leipzig, Leipzig,
Germany.
'Department of Gerontopsychiatry, Benjamin Franklin School of Medicine,
Free University Berlin, Berlin, Germany.
tivities of daily living (ADLs) and instrumental activities
of daily living (lADLs), and determining when one
requires assistance or when one's performance level
is insufficient for independent living (i.e., Katz, Ford,
Moskowitz, Jackson, & Jaffee, 1963). Furthermore,
what is known with regard to the larger picture of
daily life in old age is usually based on subjective
ratings of engagements over the last month (or some
other time interval) and concentrates on the types of
activity, disregarding the temporal, physical, and
social context of these activities (see summary from
different nations in Altergott, 1988). This focused approach
is largely because the memory performance
involved in reporting such data has been questioned
as to its veridicality. Information about how elderly
adults structure and organize their daily lives when
there are seemingly no longer any social or time constraints,
however, requires the assessment of activities
in context. In what activities do elders engage, when,
where, and with whom?
Aside from the interest in a picture of daily life in old
age, knowledge about everyday activities in old age
is important for several reasons. First, information
about everyday activities can provide insight into
elders' goals and motivation. Activities are regarded as
goal-related (Garling & Carvill, 1993; Kuhl, 1986), and
everyday activities represent the primary means by
which broader life goals are pursued and attained.
People select the activities in which they will invest
time and attention from among an enormous variety
of possible activities. Thus, the sum of choices describes
a person's day and reflects his or her immediate
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priorities and goals. These daily choices shape the
content and structure of the person's entire life. Second,
aside from reflecting one's internal constraints and
opportunities (e.g., elders' goals, motivation, skills, preferences,
needs, and wishes), everyday activities are
also affected by external opportunities and constraints
that foster and hinder participation in certain types of
activities or influence how one structures daily life. At
the most basic level, Hanson and Hanson (1993) propose
three universal principles that underlie participation
in daily activities: (a) each and every person nas
only 24 hours in a day, (b) no one can be in more than
one place at one point in time, and (c) no one can move
instantaneously from one place to another. Thus, time
and space represent the most basic influence on activity
participation. A person's day, therefore, consists of
not only the nature and diversity of activities in which
one engages, but also the dimensions of time use and
spatial reach. In addition, there is a social dimension
to "daily" or "everyday" activities (i.e., Lehr & Thomae,
1991) in the sense that social expectations, norms, roles
and functions also influence which activities will be
chosen. As such, the day represents a unit of time,
embedded in physical and social contexts, which create
opportunities and restrictions for the occurrence of
behaviors or activities. The more often these activities
are repeated at a specific time, in a specific location,
alone or with others, the more likely it is that they
become the behavioral norm for a typical day (Braudel,
1992). Thus, a random sample of tne aged population
on a person-day basis can provide a representative
picture of the everyday activities of a nation's elders
and provide a glimpse into factors that influence successful
activity engagement.
Such theoretical considerations about everyday
activities have led to specific content classifications.
The goal orientation approach is captured in the distinction
between obligatory and discretionary activities
(Moss & Lawton, 1982; Szalai, 1972) or consummatory
and instrumental activities (Garling & Garvill,
1993). The clinical practice orientation has led to the
differentiation between ADLs and lADLs (Katz et al.,
1963). From the perspective of successful aging, both
approaches to the categorization of activities are important.
In Western cultures, successful living requires
different daily activities, and engagement in those
activities that ensure personal maintenance (e.g., eating,
bathing, dressing) are considered a basic ingredient
of a successful life. The person who engages in
more than just basic activities, who takes part in the
external environment, who turns toward others, and
who engages in self-enriching activities is considered
more successful. Thus, it seems important to differentiate
between three types of everyday activities: (a)
basic activities as those pertaining to personal maintenance
in physical survival terms; (b) instrumental
activities as those referring to personal maintenance
in cultural survival terms; and (c) work, leisure and
social activities as those reflecting agentic, communal,
and self-enriching activities. Engagement in each of
these activity categories can be influenced by the
opportunities and restrictions imposed by macro- and
micro-social structures. As such, factors such as residence,
socioeconomic status, education, age, marital
status and gender can act as resources or barriers
to activity participation (Singleton, Forbes, & Agwani,
1993). Age, sex, and residential status, for instance,
are associated with basic personal activities; people in
advanced old age, women, and nursing home residents
have lower functional performance levels in
terms of ADLs and lADLs than their counterparts
(Guralnik & Simonsick, 1993; Verbrugge, 1989). Education,
income, and marital status are expected to
exert their influence on discretionary (e.g., work, leisure,
and social) activity engagement (Altergott, 1990;
Clark, 1995).
Thus, the purpose of this study was twofold: (a) to
provide a full description of daily life in the very old
that considers frequency, duration, variety, and context
of activities, and (b) to assess the effects of background
variables such as age, gender, socioeconomic
status, education, residence, and marital status on indicators
of daily activities. Because of our broader
conceptualization of "everyday," we wanted to go beyond
the usual time budget approach in our assessment
of everyday activities (Altergott, 1988; Szalai, 1972;
Verbrugge et al., 1996). Therefore, we chose to employ
an expanded time diary strategy (see also Robinson
& Nicosia, 1991) using the Yesterday Interview
(Moss & Lawton, I982). This instrument allows a minuteby-minute
reconstruction of the day preceding the
interview, enabling assessment of the frequency and
duration of activities in the activity profile, the richness
of activities (activity variety), and the breadth of
the activity contexts in terms of physical and social
environments. Given our orientation that activities
are not only an expression of needs, preferences, and
motivations but also reflect external constraints and
opportunities, we hypothesized that daily life might
look different for people with different background
variables. Age, gender, marital status, education, residential
status and SES are the most basic determinants
of living status (Elder, 1985; Hobfoll, 1988, 1989).
In sum, we address the following questions in this
article:
1. How do men and women 70 years of age and
older spend their day? In order to answer this question,
we will describe activity profiles in terms of
the frequency of activities, the duration of time
spent in activities, and the variety of activities, as
well as their physical and social contexts. Further,
similarities and differences between days of the
week (e.g., weekdays and weekend days) and
typical and atypical days will be examined.
2. How are the daily activity profile and its contexts
affected by background variables such as age, gender,
education, and marital, socioeconomic, and
residential status? First, the bivariate effects of these
personal characteristics will be examined in relation
to the frequency, duration, and variety of activities
across domains. Second, multiple regression
analyses will be used to predict activity frequency,
duration, and variety from the background variables
in order to determine the strongest predictors
of the different activity dimensions.
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Methods
Sample
This study was conducted as part of the Berlin Aging
Study (BASE; Mayer & P. Baltes, 1996). BASE was
designed to be representative of the western part of
the city of Berlin and is a probability sample of community-dwelling
and institutionalized individuals aged
70-105 years, oversampling men and the very old.
The sample was drawn from the city registration office
(in Germany, each citizen is registered) and consists
of equal numbers of men and women in six age
brackets (70-74, 75-79, 80-84, 85-89, 90-94, 95 +
years). When conducting various selectivity and representivity
analyses, P. Baltes, Mayer, Helmchen, and
Steinhagen-Thiessen (1993) demonstrated that the
present sample, which completed all 14 sessions of
the BASE protocol, maintains, by and large, its original
heterogeneity. Comparisons of the sample with the
Berlin census data on indices such as marital status,
proportion of institutionalized persons, education, and
income level showed no significant differences. The
only exception was the 12-month mortality rate: subjects
completing the full data protocol, on average,
had a lower mortality rate than dropouts.
The total sample of participants completing the
entire data protocol consists of 516 subjects with a
mean age of 84.9 years {SD = 8.7). There were 43
men and women in each five-year age bracket; 86.4%
lived in the community and 13.6% resided in longterm
care institutions (e.g., assisted living or nursing
homes). From these 516 participants, 485 had valid
data on the Yesterday Interview. A total of 31 participants
had to be excluded; 10 due to an incomplete
interview, 8 due to implausible data, 6 due to reconstruction
of less than half the day, and 2 due to data
provided mainly by a social partner present during
the interview. Of the 31 subjects, 26 had a diagnosis
of mild to moderate dementia. See Table 1 for a demographic
description of the study sample.
Measurement Instruments
Yesterday Interview.—The Yesterday Interview, developed
by Moss and Lawton (1982), was used to
assess everyday activities and their contexts. Type,
frequency, ana duration of activities engaged in by
the participant during the day preceding the interview
as well as contextual dimensions of the activity
(e.g., location and presence of social partners) were
assessed. The day started when the person awoke and
ended when he or she fell asleep. All activities mentioned
by the participants were coded into one of 44
types of activities (see Table 2).
Interrater reliabilities for activity codes yielded
Kappa coefficients above .80. These 44 activities were
then summed into eight main activity domains: basic
personal maintenance; Instrumental Activities of Daily
Living (lADLs); three types of leisure activities—television
watching, reading, and "other leisure"; social
activities; paid work; and resting. Basic personal maintenance
and lADLs were considered obligatory activities;
all other nonresting activities were considered
Table 1. Sample Characteristics (/V = 485)
Characteristic and Values
Age
70-84 years
85-103 years
Sex
Female
Male
Marital status
Married
Unmarried*
Education Level
< 10 years
£10 years
Residential Status
Private dwelling
Private house
Apartment building
Long-term care
Assisted living
Nursing home
Income
<2000 DM
>2000 DM
N
252
233
235
250
152
333
327
149
429
396
33
56
36
20
261
224
%
52.0
48.0
48.5
51.5
31.3
68.6
68.7
31.1
88.5
81.6
6.8
11.5
7.4
4.1
53.8
46.2
""Includes widowed, divorced, and never married participants.
discretionary. From the eight main activity domains,
three summary variables were created: Activity frequency
represents the total number of activities reportea
by the participant as being done in each of
the eight main categories during the previous day;
activity duration represents the total time (in minutes)
spent in the main activity categories during the day;
and activity variety is an indicator of the number of
different types of activities within one category that
are done during the day. For instance, eating, bathing,
and grooming would be three different types
of activities within the category of personal maintenance;
its activity variety woula equal 3. Waking and
falling asleep were considered the start and end of
the reported day and were not counted as separate
activities.
All geographical locations reported were coded into
seven categories: one's own room/apartment; private
building (e.g., apartment building, nursing home public
spaces); public building (e.g., bank, post office);
others' private quarters (e.g., friend's apartment); transportation
(e.g., bus, subway, car); outdoors; and
miscellaneous other. In the present analyses, we dichotomized
the activity locations into indoors versus
outdoors. This variable is used as an indicator of physical
context.
The social context of activities was also assessed.
Specifically, for each activity reported, participants
were asked, "Were you alone?" If they were not, they
were probed for who was present during the
activity: partner, children, relative, professional helper/
caregiver, friend or acquaintance, roommate, or other.
These social partner categories were coded into three
main categories: alone, with family, and with others.
Family consisted of spouse/partner, children, and
relatives; others consisted of professional helpers,
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Table 2. Activity Codes, Categories, and Percent Reporting
Main Category Code Activity Reporting
Personal 01 Arising 96.7
Maintenance 02 Personal care 97.7
03 Eating 99.8
04 Preparing for bed 98.1
05 Miscellaneous other 0.6
Instrumental 06 Shopping 38.6
Activities of 07 Light household chores 80.6
Daily Living 08 Heavy household chores 5.8
(lADLs) 09 Handiwork/mending/sewing 9.7
10 Other housework 44.1
11 Banking 1.9
12 Dealing with authorities/ 1.2
institutions
13 Dealing with the post office 6.4
14 Dealing with other official 4.7
institutions
15 Medical treatment (e.g., 13.4
getting X-rays)
16 Self-treatment (e.g., taking 19.2
a foot bath)
40 Passive transportation (e.g., 25.6
being driven)
41 Active transportation (e.g., 58.1
driving a car)
24 Reading 74.0
27 Watching TV 83.3
17 Cultural activities 18.1
18 Educational activities 1.2
19 Sports 11.3
20 Creative activities 3.5
21 Gardening 21.6
22 Walking 23.7
23 Excursions 1.6
25 Writing 5.2
26 Playing 24.1
28 Listening to radio/tape/record 28.0
29 Church activities 6.2
30 Political activities 1.0
31 Other leisure activity 11.5
Social Activities 32 Talking to people 44.9
33 Visiting 17.1
34 Telephoning 23.3
35 Other social activities 8.9
38 Helping family members 6.0
39 Helping other people 2.5
Paid Work 36 Regular paid work 2.1
37 Other work 0.8
Resting 42 Sleeping during the day 43.7
43 Doing nothing 79.2
44 Planning 14.6
Leisure Activities
Reading
Television
Other Leisure
friends/acquaintances, and roommates (e.g., in nursing
homes). In the present analyses, this social context variable
was dichotomized into alone versus with others.
Background Characteristics.—Seven types of personal
characteristics were considered in this study: age,
sex, education, marital status, residential status, and
income. For the bivariate analyses, age was coded into
three age decades (70s, 80s, and 90s+). In the regression
analyses, age was used as a continuous variable.
Sex was coded as 1 for women and 0 for men. Education
was categorized as either low (0 = fewer than 10
years of formal schooling) or high (1 = 10 or more
years of formal schooling). Marital status was dichotomized
into married (1) versus unmarried (0; included
single, divorced, and widowed participants). Residential
status was also a dichotomous variable indicating
that the participant resided either in a private residence
(coded as 0; e.g., in own home or in apartment
building) or in a long-term care setting (coded as 1;
e.g., assisted living or nursing home). Monthly income
was used as an indicator of social status and was coded
as 1 for low (<DM 2000) or 2 for high (>DM 2000).
DM 2000 is roughly equivalent to $1,500 US.
Procedures
BASE is a multidisciplinary study comprised of scientists
from the disciplines of psychology, sociology,
psychiatry, and internal medicine/geriatrics. The intensive
BASE research protocol consisted of 14 sessions.
With the exception of some medical tests (e.g., carotid
ultrasound), all sessions were conducted in participants'
homes by the same trained interviewer. The
basic resource variables used in this article were assessed
in the first interview session. During the ninth
session of the BASE intensive protocol, the Yesterday
Interview was administered. Using an interview format,
participants were prompted to recall the events
of the previous day from waking to going to sleep
(note that depending on the day of the interview,
"yesterday" could be Sunday or Monday through
Thursday). The interviewer recorded all reported information
on a standardized form. After the participant
listed all activities in the sequence and duration
they occurred, the interviewer read each activity aloud
ana asked the participant to indicate where the activity
took place and who was present during the
activity. This procedure also allowed the participant
to add activities that might have been forgotten initially.
At the end of the interview, participants were
asked to state whether the reconstructed day was a
typical or atypical day. Interviewers also recorded the
day of the week that "yesterday" represented.
Results
First, similarities and differences between typical and
atypical days and between weekdays and weekends
(Sundays) will be examined. Second, an average activity
profile will be described in terms of frequency,
duration, and variety of activities, as well as the social
and physical context of activities. Note that in all these
comparisons the time between midnight and 5:59 in
the morning was not assessed. Third, bivariate relationships
between background variables and activity
frequency, duration, and variety will be examined
using t tests and analysis of variance. Finally, activity
frequency, duration, and variety across activity domains
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will be predicted from the background variables using
multiple regression.
Similarities and Differences Between
Typical Versus Atypical Days and
Weekdays Versus Weekends
With regard to typicality of the reconstructed day,
the majority of participants defined their reported
"yesterday" as typical (71.5%) rather than atypical (28.5%).
Typical and atypical days were characterized more by
their similarities than their differences. No differences
were noted in time spent in personal maintenance,
socializing, working, resting, or television watching.
On atypical days, significantly more time was spent
doing "other leisure" activities (140 min vs 106 min)
and lADLs (211 min vs 168 min) and less time was
spent reading (75 min vs 100 min). In addition, more
time was spent outdoors (217 min vs 166 min) and
with others (90 min vs 49 min) on atypical days [all F
values (1,483) > 6.8, p < .01]. There were no significant
differences in income, level of education, or
residential status across typical or atypical days. Significant
age and gender effects, however, were noted
for typical versus atypical days. Participants reporting
typical days were significantly older than those describing
atypical days (mean age = 82 vs 85 years)
and were more likely to be men [%2
= 13.99, df = 1,
p < .001].
Differences between weekdays and Sundays were
also examined. Only time spent performing lADLs [F
(1,483) = 25.0, p < .001)] and outdoors [F(1,483) =
11.3, p < .001)] differed significantly between weekdays
and Sundays. More time was spent on lADLs
(38 min vs 12 min) and outside the home (196 min
vs 129 min) on weekdays than on weekends. Because
these differences between typical and atypical and
weekdays and weekend days were few and relatively
small in magnitude (average difference in specific activities
was about 30 min), data were aggregated across
all days in the analyses that follow.
A Description of Yesterday
Frequency and Duration of Activities.—Figure 1 presents
the average profiles of activities for this sample
of adults aged 70 and older. On the upper left, the
mean number of activities reported in each of the main
activity categories is presented. On the upper right,
durations of activities (in minutes spent) are presented.
ACTIVITY FREQUENCY
Missing (0.4)
Resting (2.7)
Paid Work (0.1)
Social (1.6)
Television (1.7)
Reading (1.7) YA
Other leisure (2.3)
ACTIVITY DURATION (in minutes)
Missing (15)
Resting (177)
Personal care (150)
Personal care (7.9)
Paid Work (8)
Social (67)
Television (163)
Other leisure (116)
Reading (93)
ACTIVITY VARIETY
Resting (1.4)
Social (1.0)
Television (0.8)
Reading (0.7)
Other leisure (1 6)
Figure 1. A description of how elders spend the day.
Personal care (3.9)
IADL (3.1)
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It should be noted that, on average, participants reported
that their day was about 16 hours long {SD =
1.8 hrs, range = 10-22 hrs).
As can be seen, the frequency profile is quite different
from the duration profile. For instance, basic
personal maintenance activities are the most frequently
occurring activities (mean = 7.9 activities) engaged in
during the day, but they do not occupy the largest
amount of time. Instead, the day is dominated by time
spent watching television (17% of the day), instrumental
activities (15% of the day), and other leisure activities
(12% of the day). Indeed, the leisure activities of reading,
television watching, and "other" occupy about one
third of the waking day. In addition, resting consumes
a substantial portion of the daytime (19% of the day).
Thus, it is important to look at both frequency and
duration data to get a complete picture of how the
average day is spent.
It should be noted, however, that there is substantial
variability in the amount of time spent with different
activities during the day. Table 3 summarizes the
means, standard deviations, and ranges across the different
activity domains. Greater variability about the
mean is observed for the duration variables than for
the activity frequency or variety dimensions.
Variety Within Activities.—Variety in activities is a
measure that goes beyond the basic level of the presence
or absence of activities during the day by providing
additional information about the diversity and
richness of activities in which elderly adults engage.
The variety of activities on an average day is presented
in Figure 1 (bottom graph). Not surprisingly, the profile
of activity variety appears quite similar to activity
frequency because there is likely to be greater variety
in activity categories that occur more often during the
day. On average, participants reported doing about
four different basic personal maintenance activities
(mean = 3.9, range = 1-5) and three different lADLs
(mean = 3.1, range = 0-8). Substantially less variety
was noted in the overall leisure and social domains.
Across all activity categories, participants averaged
about 13 different activities during the day (mean =
12.6, range = 4-20).
Social and Physical Context.—With regard to time
spent with social partners, most of the day was spent
alone (mean duration = 626 min; about 10.5 hrs),
followed by time spent with one's spouse/partner
(mean duration = 173 min; about 3 hrs; see Figure
2). Time spent with roommate(s) or groups of people
and professional caregivers consumed the next largest
amount of time during the day, each representing
about 40-50 minutes out of the day. The least amount
of time was spent during the day with children, relatives,
and friends; mean duration of time spent with
each of these three categories of social partners was
15-20 minutes.
Elderly participants, on average, reported spending
most of their day (about 12 hrs, 80% of day) in their
own homes (including apartments and nursing homes).
Less than one fourth (18.7%) of the day was spent
outdoors (see Figure 2).
Bivariate Relationships Between Background
Variables and Activity Indicators
Activity Frequency.—To examine the effects of background
variables on activity duration, a series of univariate
analyses of variance and t tests were done. Table
4 presents the mean frequency and significance tests
for specific activities for each of the separate group
comparisons. Due to the number of comparisons being
made, only differences significant at the p < .001
level are indicated. As Table 4 shows, significant age
group differences were noted in five of the eight activity
categories. Participants older than age 90 performed
significantly fewer instrumental activities, and
fewer reading, television, and other leisure activities,
but did more resting activities during the day [all F
values (2,482) > 7.0, p < .001].
The same pattern of results was noted for residential
status. Residing in a long-term care facility was associated
with significantly fewer instrumental activities
performed during the day as well as fewer reading,
television watching, other leisure, and paid work activities.
In addition, long-term care residents had significantly
more resting periods [all t statistics range from
4.2-8.8, p < .001].
Table 3. Description of Activity Frequency, Duration, and Variety Across Domains
Activity
Category
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
Frequency
(No. of activities)
Mean (SD)
15.6 (5.6)
7.8 (1.8)
7.6 (5.1)
7.2 (3.5)
1.7 (1.2)
1.7 (1.4)
2.3 (2.0)
.7 (.06)
1.6 (1.6)
2.7 (2.1)
Range
3-36
3-16
0-30
0-22
0-7
0-7
0-11
0-7
0-10
0-13
Duration
(Min spent in i
Mean (SD)
337.6 (146.0)
150.1 (57.6)
180.0 (127.8)
439.0 (179.1)
162.7 (129.4)
93.3 (95.5)
115.8 (115.8)
7.4 (50.1)
67.2 (84.2)
177.2 (167.1)
ictivities)
Range
75-840
34-510
0-565
0-985
0-750
0-474
0-565
0-580
0-495
0-925
Variety
(No. of
(different activities)
Mean (SD)
7.1 (1.9)
3.9 (.3)
3.1 (1.8)
4.2 (1.8)
.8 (.4)
.7 (.4)
1.6 (1.2)
.1 (.2)
1.0 (.8)
1.4 (.7)
Range
1-12
0-5
0-8
0-9
0-1
0-1
0-6
0-1
0-4
0-3
Vol. 38, No. 5, 1998 561
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WITH WHOM THE DAY IS S PENT (in % of day)
Other (5.4%)
Professional (4.
Relative (2.8%)
Children (2.2%)
Partner (18.1%)
Friends (1.6%)
Missing (1.3%)
WHERE THE DAY IS S PENT (in % of day)
Other (0.5%)
In transit (2.5%) Missing (1.3%)
Someone's apt. (2.5%)^
Public spaces (3.7%)^
Outdoors (7.2%)
In building (2.1%)
Alone (64.4%)
Primary living space (80%)
Figure 2. The social and physical contexts in which activities occur.
Gender and marital status had significant bivariate
relationships only for the frequency of IADL tasks and
television watching. Women reported more instrumental
activities and had fewer television watching periods;
married people did fewer IADL tasks but watched television
more often during the day [t values range from
-4.0-4.3, p < .001].
Level of education and income had no significant
relationship with the specific activity domains, but
more education was associated with more frequent
discretionary activities [t(474) = -4.9, p < .001; see
Table 4).
Activity Duration.—With regard to time spent in
activities, group differences based on sociodemographic
characteristics were noted for basic personal maintenance,
instrumental activities, other leisure activities,
television watching, and resting (see Table 5). Significant
effects of age were noted for four categories of
activities: personal maintenance, instrumental, other
leisure, and resting [all F values (2,482) > 8.2, p <
.001]. Participants older than the age of 90 spent
significantly more time doing basic personal maintenance
tasks than did participants in their 70s and 80s.
In contrast, there were significant incremental differences
between each age decade in the amount of
time spent in instrumental and other leisure activities,
which were lowest in the 90s and highest in the 70s,
and in time spent in resting, which showed the reverse
pattern.
Residential status showed a similar pattern of effects.
Long-term care residents spent significantly less
time doing lADLs, watching television, and doing other
leisure activities, but more time resting. None of the
long-term care residents were engaged in paid work,
[all t values range from 3.4-11.0, p < .001].
Gender was significantly associated with instrumental
activities and television watching. Women
spent significantly more time doing lADLs [t(463.7) =
-5.7, p < .001] and less time watching television
[£(483) = 3.3, p < .001] than men.
With regard to marital status, the reverse pattern
was noted. Married participants spent significantly
more time watching television and less time doing
IADL tasks and resting. There were no significant
effects of education or income on the amount of
time spent in activities [all t values range from -3.7 to
3.9, p < .001]
Table 4. Effect of Background Characteristics on the Mean Frequency of Activities
A s*tiwit"\/
Activity
Category
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
*p < .001.
a
90+ age group
70s
17.1
7.8
9.1
8.4
1.9
1.8
2.8
0.2
1.9
2.0
Age Decade
80s
15.9
7.8
8.1
2.5
1.8
1.8
2.3
0.1
1.6
2.5
90s+
13.6
8.0
5.6
3.6
1.4
1.3
1.6
0.0
1.4
3.6
differs significantly
P
*a
*a
• a
• a
*a
*a
*a
from
Gender
Male
14.6
7.8
6.7
7.5
1.9
1.8
2.2
0.1
1.5
2.5
the 70:
Female
16.6
8.0
8.6
6.9
1.5
1.5
2.3
0.0
1.7
2.8
> and 80s
Married
p No
* 16.0
7.8
* 8.1
6.9
* 1.6
1.6
2.2
0.1
1.6
2.9
Yes p
14.6
8.0
6.5 *
7.9
2.0 *
1.9
2.4
0.1
1.7
2.2 *
age groups.
562
Education
Low
15.4
7.9
7.5
6.8
1.7
1.5
2.1
0.0
1.5
2.8
High p
16.1
7.8
8.1
8.4 *
1.8
2.0
2.7
0.2
2.0
2.4
Long-Term Care
No
16.1
7.8
8.1
7.6
1.8
1.8
2.4
1.0
1.7
2.4
Yes p
12.4 *
8.3
4.1 *
4.4 *
.9 *
.9 *
1.4 *
0.0
1.3
5.0 *
The
Income
Low
15.5
7.9
7.6
7.0
1.6
1.6
2.1
0.0
1.7
2.8
High p
15.7
7.9
7.7
7.5
1.8
1.7
2.5
1.1
1.6
2.6
Gerontologist
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Table 5. Effect of Background Characteristics on the Mean Duration of Time Spent in Activities
Activity
Category
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
70s
371.3
138.6
216.6
226.5
174.5
100.4
146.3
16.1
80.1
112.9
Age Decade
80s
336.0
148.9
182.1
179.1
177.6
101.7
117.4
5.0
61.6
157.3
90s+
301.9
164.2
137.3
139.2
133.4
76.5
80.2
0.4
59.0
270.0
P
* b
*a
* b
* b
* b
*b
Male
310.5
151.8
148.5
184.0
181.4
100.9
116.9
10.2
67.1
170.7
Sex
Female p
366.3 *
148.4
213.5 *
192.0
142.7 *
85.3
114.6
4.4
67.4
184.2
Married
No
348.1
148.6
192.5
176.2
148.2
88.6
114.0
7.0
62.1
194.3
Yes
314.5
153.6
152.6
198.0
194.5
103.7
119.7
8.3
78.3
139.9
Education
p Low
333.7
151.3
* 179.3
171.0
* 171.2
88.1
108.3
4.1
62.7
* 189.2
High
362.7
141.3
188.3
207.9
141.8
104.6
131.4
15.1
76.4
146.0
Long-Term Care
p No
351.3
148.2
193.8
459.1
172.1
98.0
121.5
8.4
53.2
150.3
Yes p
240.0 *
165.6
74.3 *
285.0 *
90.1 *
57.5
72.1 *
0.0
65.4
383.6 *
Income
Low
326.9
148.5
175.3
433.9
177.2
88.7
107.0
5.3
67.9
183.7
High p
350.0
152.0
185.5
445.0
147.1
98.2
125.3
9.7
66.4
170.0
*p < .001.
"90+ age group differs significantly from the 70s and 80s age groups.
b
AII three age groups differ significantly.
When the eight activity domains (excluding resting)
were condensed into two main categories of activities—obligatory
vs discretionary activities—a similar pattern
emerged (see Table 5). Significant incremental age
differences were found for the time spent in both categories
of activities. Those older than 90 spent less
time in both obligatory and discretionary activities than
those in their 80s, who spent less time doing both
types of activities than those in their 70s.
In addition, being female was associated with increased
time doing obligatory activities. Long-term care
residence was associated with less time spent in both
obligatory and discretionary activity domains. There
were no significant effects of income or education in
the amount of time spent in either obligatory or discretionary
activities.
Activity Variety.—When the variety or the number
of different activities is examined, the pattern of results
is similar to that noted for duration of activities.
For ease of presentation, only results for the obligatory,
discretionary, and resting domains will be described.
With regard to age decades, participants in
their 90s and older do significantly fewer different obligatory
activities [F(2,482) = 26.3, p < .001] and discretionary
activities [F(2,482) = 25.3, p < .001] than
those in their 70s or 80s, but show more variety in
resting activities. Specific activity differences are
shown in Table 6. No significant effects of sex, income,
or education on activity variety were noted.
There was a significant effect of residential status in
that those living in long-term care had significantly less
variety in both obligatory [£(483) = 7.3, p < .001]
and discretionary activities [£(483) = 7.7, p < .001]
than community-dwelling elders.
Social Context.—In univariate analyses of group differences,
the social interconnectedness of the elderly
participants was dependent on sex, marital status,
and residence (see Table 7). Women [£(468.1) =
-8.1, p < .001] and unmarried elders [£(483) = 18.1,
p < .001] spent significantly more time alone. Longterm
care residents and women spent significantly
less time with family. No significant influences of age,
education, and social status were noted.
Physical Context.—No significant effects of sex, education,
marital, residential, or social status were noted
Table 6. Effect of Background Characteristics on the Mean Variety of Activities
Activity
Category
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
70s
7.6
3.9
3.6
4.8
0.9
0.8
1.9
0.1
1.2
1.2
Age Decade
80s
7.2
4.0
3.3
4.3
0.9
0.8
1.6
0.0
1.0
1.4
90s+
6.2
3.9
2.3
3.4
0.8
0.6
1.2
0.0
0.9
1.6
P
*a
*a
*a
*a
*a
*a
Male
6.8
3.9
2.9
4.2
0.9
0.8
1.6
0.0
0.9
1.4
Sex
Female p
7.2
3.9
3.3
4.2
0.8
0.7
1.6
0.0
1.1
1.3
Married
No
7.3
3.9
3.2
4.1
0.8
0.7
1.5
0.0
1.1
1.4
Yes p
6.8
3.9
2.8
4.4
0.9 *
0.8 *
1.6
0.0
1.0
1.3
Education
Low
7.0
3.9
3.0
4.0
0.8
0.7
1.5
0.0
0.9
1.4
High p
7.3
3.9
3.3
4.7
0.8
0.8
1.8
0.1
1.3 *
1.3
Long-Term Care Income
No
7.2
3.9
3.3
4.4
0.9
0.8
1.7
0.0
1.1
1.4
Yes p Low
5.6 *
3.8
1.8 <
2.6 *
0.5 *
0.4 *
0.8 *
0.0
0.8
1.5
* 7.0
4.0
* 3.0
* 4.1
K
0.8
K
0.7
* 1.5
0.0
1.0
1.4
High p
7.1
3.9
3.2
4.3
0.8
0.7
1.7
0.0
1.0
1.4
*p < .001.
a
90+ age group differs significantly from the 70s and 80s age groups.
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Table 7.
Age Decade
Effect of Background Characteristics on the Contexts of Activities
Sex Married Education Loi
Activity
Category
Education Long-Term Care Income
70s 80s 90s+ p Male Female p No Yes p Low High p No Yes p Low High p
Social Contexts
Time alone 581.6 652.5 644.8 522.7 735.0 * 758.1 335.2 * 639.0 591.8 612.3 727.4 612.6 640.6
Time with
family 273.2 199.0 186.9 330.3 104.6 * 89.3 509.3 * 215.1 246.3 241.3 64.9 * 227.8 212.9
Time with
others 71.1 66.6 44.2 45.2 78.0 * 76.4 27.6 * 55.1 72.7 59.7 72.1 56.8 66.2
Physical Contexts
Time
outdoors 253.7 181.2 100.9 *a
185.4 176.7 180.4 182.7 182.0 179.9 182.7 169.9 175.4 187.8
*p < .001.
a
All three age groups differ significantly.
on the amount of time spent outdoors (see Table 7).
The only significant effect was noted for age. Significantly
less time was spent outdoors [F(2,482) = 29.4,
p < .001] with each successive age decade.
Predicting Activity Frequency,
Duration, Variety, and Context
Given the number of bivariate relationships between
sociodemographic characteristics and different
aspects of everyday activities, the question remains as
to which factors are more important in predicting activities.
Thus, a series of multiple regression analyses
were conducted to address this question. In the first
set of analyses, activity frequency in each of the eight
domains, plus the summary categories of obligatory
and discretionary activities, was regressed on age, sex,
marital status, long-term care, education level, and income.
Second, the same series of multiple regression
models was conducted with activity duration as the
dependent variable. Finally, in the third set of models,
activity variety in the different activity categories
was the dependent variable. The results of the multiple
regression analyses are presented in Table 8. Again,
given the number of comparisons being made, only
predictors significant at the .001 level are reported.
With regard to activity frequency, significant regression
models were noted for all categories of activities
except basic personal maintenance, reading, and social
domains. The most consistent predictors of the
number of activities in all other domains were age
and residential status. With increasing age, the frequency
of doing lADLs, watching television, and doing
other leisure activities decreased significantly while
the frequency of resting activities increased. Residence
in a long-term care facility showed the same pattern
of results as age. In contrast, marital status, education
level, and income had very little effect after controlling
for all other demographic characteristics, and sex
had no significant influence.
When the same set of personal characteristics was
used to predict activity duration, age and residential
status again emerged as significant predictors of time
in most activity domains after controlling for the other
independent variables. Advanced age was associated
with significantly less time spent in instrumental activities,
other leisure activities, and paid work and with
more time spent doing personal maintenance and
resting. Long-term care residents spent significantly less
time in instrumental activities and watching television,
but more time resting. A higher level of income was
associated with more time spent in paid work, holding
all other variables constant. Finally, being female
was associated with more time spent in instrumental
activities, even after controlling for age, marital status,
long-term care residence, and level of income and edu-
cation.
When activity variety was examined, again age and
long-term care residence were the strongest predictors,
after controlling for all others in the regression
models. In particular, living in a long-term care facility
was associated with significantly less variety in the types
of activities engaged in during the day.
In contrast, there were very few significant predictors
of the amount of time spent in different social
and physical contexts when all sociodemographic predictors
were entered in the models (see bottom of
Table 8). Only advanced age was significantly predictive
of less time spent outdoors. Marital status was significantly
associated with less time spent alone, after
controlling for all other variables.
Discussion
The goal of this article was to explicate how elderly
adults spend their days and what background characteristics
influence their daily activities. To that end,
a detailed profile of everyday life that describes the
frequency, duration, variety, and social and physical
contexts of activities and their association with personal
characteristics such as age, sex, marital status,
gender, education, income, and residential status has
been presented here. The description of the type, location,
and social context of activities engaged in by
elderly adults, as well as the influence of background
variables on that engagement, presents a first glimpse
into the everyday "competence" of elders over the
age of 70.
564 The Gerontologist
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Table 8. Background Characteristics Regressed on Frequency, Variety, and Duration of Activities
Predictors3
Dependent
Variables
Model
R2
Age Sexb
Marital
Statusc
Long-Term
Cared
Education
Level
Income
Level
Activity Frequency
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
Activity Duration
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
Activity Variety
Obligatory
Personal
IADL
Discretionary
Watching TV
Reading
Other leisure
Paid work
Socializing
Resting
Activity Context
Time alone
Time outdoors
.14
.01
.18
.20
.11
.08
.10
.10
.04
.22
.16
.05
.21
.20
.09
.04
.07
.11
.02
.30
.18
.03
.17
.19
.13
.11
.11
.13
.06
.07
.41
.13
-.25 —
— —
-.28 —
-.26 —
-.16 —
— —
-.20 —
— —
— —
.22 —
-.16 .21
.20 —
-.21 .23
-.27 —
— —
— —
-.20 —
-.16 —
— —
.28 —
-.29 —
— —
-.28 —
-.24 —
— —
— —
-.17 —
— —
— —
.23 —
— —
-.36 —
-.17
-.60
-.16
-.20
-.21
-.17
-.16
.32
-.21
-.26
-.22
-.16
.36
-.22
-.20
-.24
-.29
-.19
-.17
.19
.29
.21
.30
.15
.33
'Standardized regression coefficients of significant predictors; p <
b
Coded 0 = male, 1 = female.
c
Coded 0 = single, 1 = married.
d
Coded 0 = no, 1 = yes.
How do older adults spend their day? Basically, time
is spent in one of three activity categories: obligatory
activities (personal self-maintenance and lADLs), leisure
(television viewing, reading, or other activities),
or resting. The relative distribution of these activities
across the day depends on what dimension of the
activity one is describing: frequency, duration, or variety.
Obligatory activities such as personal self-maintenance
and instrumental activities occur most frequently
and had the highest variety during the day,
yet do not occupy the most time. Instead, the day is
temporally dominated by leisure activities, especially
watching television. On average, about half of the average
number of 16 waking hours in the day were
spent in pursuit of leisure activities. In terms of variety
.001.
of activities, however, leisure was restricted mostly to
television viewing and reading. About one third of the
day was spent doing 16 obligatory activities that participants,
on average, reported. Finally, resting occupied
a significant portion of the day (about 3 hours,
representing 19% of the waking hours). The vast majority
of the day was spent alone and at home.
What factors influence how elders spend their
time? By and large, age has the most consistent bivariate
effect on activity frequency, duration, and variety.
Across all activity categories, elders older than the age
of 90 show significantly less activity engagement than
their younger peers. Trie only exception to this finding
is in terms of resting; the oldest-old spend significantly
more time resting than younger elders do. This
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finding likely reflects increasing frailty in advanced old
age.
Gender and marital status had relatively few significant
effects on how elderly people spend their day.
Women and unmarried persons reported spending
more time engaged in IADL tasks (e.g., doing housework,
running errands, and using transportation) and
less time watching television than others. Because marital
status and gender are strongly related in late life,
this finding applies to widowed women and reflects
patterns of gender-role socialization. Further, elderly
women and unmarried persons spend significantly
more time alone and at home. As such, gender and
marital status influence the social and physical contexts
of activities. Somewhat surprisingly, education
showed few effects on activities and income level had
no significant effect at the bivariate level.
Residential status also exerted a significant influence
on how participants spend their days in terms of frequency,
duration, and variety of activities. Participants
residing in long-term care housing did fewer IADL tasks
and watched less television, and they read and did
other leisure activities less often and for shorter times
during the day. In addition, long-term care residents
rested longer and more often than their communitydwelling
peers. There are several explanations for this
finding. First, restricted leisure activity participation may
reflect the high level of impairment and frailty among
those who do not, and most likely are unable, to live
independently. Discretionary activities are usually the
first ones to be selected out when impairments strike
(B. Baltes & M. Baltes, 1990). In one sense, they are
the easiest to drop from one's behavioral repertoire
because they do not ensure one's survival and are
less likely to endanger one's status of autonomy and
independence. Second, long-term care facilities represent
a restricted opportunity structure (M. Baltes &
Horgas/1997). For example, it would be expected
that long-term care residents do fewer IADL tasks like
shopping and cleaning because, in many cases, people
moved into these living arrangements because they
did not want, or were unable, to do these activities.
In this regard, the long-term care facility provides a
compensatory environment. In other analyses, M. Baltes
and Horgas (1997) found that elderly long-term care
residents spent the same amount of time doing basic
self-care activities as community-dwelling elders, but
did fewer different basic activities during that time,
thus lending further support to the compensation hypothesis.
Third, residential status is undoubtedly confounded
with age. Post hoc analyses indicated that
age and residential status were significantly related. Of
participants residing in long-term care housing, 66%
were 90 years of age or older compared with 25% of
those participants in their 80s and 25% of those in
their 70s.
Given the pattern of bivariate relationships between
the personal background variables and the different
activity dimensions and categories, the question remains
as to which factors were the strongest predictors
of the activity variables. Across the multiple regression
analyses, age and residential status showed the strongest
and most consistent associations with activities,
representing the restriction of both internal and external
resources. Age as an internal resource reflects the
underlying health or cognitive status of elders. It should
be noted that very old chronological age seems to
make a unique contribution to the level of disability
and activity when disease and functional limitations
are controlled for (Steinhagen-Thiessen & Borchelt,
1996). Residential status reflects external resources
in that opportunities to exhibit some types of behaviors
are, on average, fewer in institutions than in the
community.
In general, age and long-term care residence appear
to be the most influential barriers to how older
adults conduct daily life. Whereas advanced age, as a
proxy for health and cognition, represents a selection
mechanism for activity engagement, long-term care may
be both a selection and a compensation mechanism
for prior physical decline and inability to perform activities
(see also M. Baltes, Wahl, & Reichert, 1991).
The differential patterns of age-related and residencerelated
activity differences across discretionary and
obligatory activity domains are consistent with the
model of selective optimization with compensation (P.
Baltes & M. Baltes, 1990). That is, as people experience
age-related decline, they restrict the domains of
activities in which to concentrate their energy and
efforts, thereby maintaining and perhaps even optimizing
performance in these activities. Leisure activities,
for instance, are not associated with physical
survival or independent living, so elders who are experiencing
functional difficulties may choose to drop
some of these activities from their daily routines. From
the perspective of successful aging, this is viewed as a
mechanism for adaptation in late life.
Alternatively, these results may reflect the nature of
the long-term care environment. Low levels of activity
engagement may be a consequence of the relative
paucity of opportunities for enriching activities, or at
least activities that are congruent with elders' preferences
and abilities. In these data, we find that institutionalized
elders engage in less activity, be it instrumental
or leisure in nature. Instead, they rested more
frequently during the day. However, if one uses a typicality
variable as an indicator of a "special" day—one
that is characterized by more time outdoors, spent with
others, and engaged in leisure activities—communitydwelling
elders were no more likely to report such a
day than those residing in long-term care facilities. Thus,
although many nursing homes and sheltered care
facilities undoubtedly provide limited opportunities for
discretionary and IADL activities, a restricted activity
level appears to be more the cause than the consequence
of institutionalization in these data. That is,
for those older than the age of 90, the external environment
may make less of a difference in whether a
behavior occurs than intraindividual factors. Longitudinal
data would be required, however, to test this
assertion.
Perhaps the more interesting finding from these
data is the substantial variety in how elders spent their
time. For instance, very few participants reported
doing paid work (mean < 1 hr).Yet at least one participant
stated that he or she spent about 9.5 hours
566 The Gerontologist
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doing paid work. In contrast, IADL activities, such as
household chores and doing errands, averaged about
3 hours with a standard deviation of about 2 hours.
Again, at least one person reported spending about
9.5 hours doing these tasks whereas others reported
spending no time on these activities. These findings
support the heterogeneity of aging that reflects lifelong
activity patterns as well as gender roles and personal
preferences. The amount of time spent doing
diverse activities provides some insight into individual
goals and motivations, needs and desires, and successful
aging. Spending the bulk of one's day resting, for
instance, would generally indicate a person who is
aging less well than an individual who engages in a
variety of activities, some obligatory and some discretionary,
during the day.
Although these findings are specific to elderly Germans,
there is some evidence to suggest their generalizability
to North American elders. The literature that
would provide contrasting or supporting data about
elders' activities is sparse at best. The most direct comparison
would be the study conducted by Moss and
Lawton (1982) that used the same instrument to assess
activities in a younger sample. Those data were
remarkably similar to the results reported in this article.
In both the German and the American descriptions
of daily life, the majority of the day was spent
alone and at home (or in the primary living space).
Among leisure activities, television viewing predominates
in both cultures (Fouts, 1989; Moss & Lawton,
1982), but German elders spent about 1 hour less
watching TV and 1 hour more outdoors than did American
elders, even though the German sample was an
average of 10 years older (mean = 85 years) than the
American sample (mean = 76 years). This may reflect
cultural differences in urban planning and housing
designs that emphasize outside spaces such as parks
and gardens. The influence of American long-term care
institutions on elders' activities is consistent with the
findings reported here (Moss & Lawton, 1982; Resnick,
Fries, & Verbrugge, 1997). Gender differences, especially
in the IADL domain, are more likely to reflect
cohort effects than culture and would be relatively
consistent across these two Western cultures. As such,
cultural influences are more likely to exert their effects
at the individual level and would be attenuated
in the average profiles presented here.
The limitations of this study should be noted. First,
the data reported here are cross-sectional and do not
provide information about the intraindividual stability
or change in activities. In addition, these data cannot
address the temporal ordering of influences; that is,
the question of whether the long-term care environment
constrains activities or restricted activities lead
to institutionalization cannot be answered. Longitudinal
change in activity profiles will be addressed in the
BASE follow-up study that is currently in progress. Further,
the results reported here focus only on descriptive
analyses of activities and demographic and contextual
influences on activity profiles. Our future work
will also examine the influences of health and cognitive
status indicators on elders' activities.
Understanding how elderly adults spend their days
provides a glimpse into the everyday world of one of
the fastest growing segments of the population. As
such, insight into similarities and differences in activity
patterns, as well as the vast heterogeneity in how a
day is spent, supports a multidimensional and individualized
view of aging. In addition, these findings
provide some insight into how personal resources,
such as living independently, being younger, and having
a marriage partner, shape not only how we spend
our days, but also where and with whom we spend
them. Recognition of these factors may help to draw
attention to the need for compensatory mechanisms
to support successful aging in face of the loss of social
resources often associated with aging.
References
Altergott, K. (Ed.). (1988). Daily life in late life: Comparative perspectives.
Newbury Park, CA: Sage.
Altergott, K. (1990). Age, gender, and daily life: An analysis of social
involvements. Special issue: Research on time use. Social Indicators
Research, 23(4), 367-380.
Bakes, M. M., & Horgas, A. (1997). Long-term care institutions and the
maintenance of competence. In K. W. Schaie & S. Willis (Eds.), Societal
mechanisms for maintaining competence in old age (pp. 142-
164). New York: Springer.
Baltes, M. M., Wahl, H.-W., & Reichert, M. (1991). Institutions and
successful aging for the elderly? Annual Review of Gerontology and
Geriatrics, 11, 311-337.
Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful
aging: The model of selective optimization with compensation.
In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectives
from the behavioral sciences (pp. 1-34). New York: Cambridge
University Press.
Baltes, P. B., Mayer, K. U., Helmchen, H., & Steinhagen-Thiessen, E.
(1993). The Berlin Aging Study (BASE): Overview and design. Aging
and Society, 13, 483-515.
Braudel, F. (1992). The structures of everyday life (Vol. 1). Berkeley:
University of California Press.
Clark, D. O. (1995). Racial and educational differences in physical activity
among older adults. The Gerontologist, 35, 472-480.
Elder, C. H., Jr. (Ed.). (1985). Life course dynamics. Ithaca, NY: Cornell
University Press.
Fouts, C. T. (1989). Television use by the elderly. Canadian Psychology,
30(3), 568-577.
Carling, T., & Carvill, J. (1993). Psychological explanation of participation
in everyday activities. In T. Carling & R. C. Colledge (Eds.),
Behavior and environment: Psychological and geographical approaches
(pp. 270-297). Oxford: Elsevier Science.
Curalnik, J. M., & Simonsick, E. M. (1993). Physical disability in older
Americans. Journals of Gerontology, 48 (Special issue), 3-10.
Hanson, S., & Hanson, P. (1993). The geography of everyday life. In T.
Carling & R. C. Colledge (Eds.), Behavior and environment: Psychological
and geographical approaches (pp. 249-269). Oxford: Elsevier
Science.
Herzog, A. R., Kahn, R. L, Morgan, J.N., Jackson, J.S., & Antonucci,
T. C. (1989). Age differences in productive activities. Journals of Gerontology,
44, S129-138.
Hobfoll, S. E. (1988). The ecology of stress. New York: Hemisphere
Publishing.
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing
stress. American Psychologist, 44, 513-524.
Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., & Jaffe, M. W.
(1963). Studies of illness in the aged. Journal of the American Medical
Association, 185, 914-919.
Kuhl, J. (1986). Aging and models of control: The hidden costs of wisdom.
In M. M. Baltes & P. B. Baltes (Eds.), 77>e psychology of control
and aging (pp. 1-33). Hillsdale, NJ: Erlbaum.
Lehr, U.M., & Thomae, H. (1991). Alltagspsychologie (The psychology
of everyday life]. Darmstadt: Wissenschaftliche Buchgesellschaft.
Mayer, K.-U., & Baltes, P.B. (1996). Die Berliner altersstudie (The Berlin
Aging Study]. Berlin: Akademie Verlag.
Moss, M., & Lawton, M. P. (1982). Time budgets of older people: A
window on four lifestyles. Journals of Gerontology, 35, 576-582.
Resnick, H. E., Fries, B. E., & Verbrugge, L. M. (1997). Windows to
their world: The effect of sensory impairments on social engagement
and activity time in nursing home residents. Journal of Gerontology:
Social Sciences, 52B, S135-S144.
Robinson, J. P. & Nicosia, F. M. (1991). Of time, activity, and consumer
behavior: An essay on findings, interpretations, and needed research.
Journal of Business Research, 22, 171-186.
Vol. 38, No. 5,1998 567
Downloadedfromhttps://academic.oup.com/gerontologist/article-abstract/38/5/556/593810byMasarykovauniverzita,Fakultasocialnichstudiiuseron17September2018
Singleton, J. F., Forbes, W. F., & Agwani, N. (1993). Stability of activity
across the lifespan. Activities, Adaptation and Aging, 18, 19-27.
Steinhagen-Thiessen, E., & Borchelt, M. (1996). Morbiditat, medikation
und funktionalitat im alter [Morbidity, medication, and functioning
in the elderly). In K. U. Mayer & P. B. Baltes (Eds.), Die Berliner
altersstudie (pp. 151-184). Berlin: Akademie Verlag.
Szalai, A. (Ed.). (1972). The use of time. The Hague: Mouton.
Verbrugge, L. M. (1989). Gender, aging, and health. In K. S. Markides
(Ed.), Aging and health: Perspectives on gender, race, ethnicity, and
class (pp. 23-78). Newbury Park, CA: Sage.
Verbrugge, L. M., Gruber-Baldini, A. L, & Fozard, J. L. Age differences
and age changes in activities: Baltimore Longitudinal Study of Aging.
Journal of Gerontology: Social Sciences, 51B, 30-41.
Received September 5, 1997
Accepted August 4, 1998
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