Journal of Gerontology: SOCIAL SCIENCES
1996, Vol. 5IB, No. 1, S3O-S41
Copyright 1996 by The Gerontological Society of America
Age Differences and Age Changes in Activities:
Baltimore Longitudinal Study of Aging
Lois M. Verbrugge,13 Ann L. Gruber-Baldini,23 and James L. Fozard3
'Institute of Gerontology, The University of Michigan.
department of Epidemiology and Preventive Medicine, University of Maryland at Baltimore.
'Longitudinal Studies Branch, Gerontology Research Center, National Institute on Aging, Baltimore.
This study examines cross-sectional age differences, longitudinal age changes, and secular changes in obligatory,
committed, and discretionary activities, using activity questionnaires completed by men and women participants in the
Baltimore Longitudinal Study of Aging between 1958 and 1992. (1) Time spent, on obligatory activities and passive
leisure is greatest, and on committed activities and active leisure least, for older adults. (2) Longitudinal patterns
usually mirror cross-sectional ones. There are pronounced exceptions for women whose paid work time has been
increasing and housework decreasing, while cross-sectional patterns show the reverse. (3) Over recent decades, time
in committed activities shifted in opposite ways for men and women. Men decreased paid work and increased
housework, repairs and yardwork, shopping, and child-care, while women increased paid work and decreased
housework. In sum, the age structure of activities has persisted in the midst of new social opportunities; gender roles
have proven more malleable than age roles.
people age, their activities change due to shifts in
AS preferences,
constraints, abilities, and health. The
L
changes occur in many ways — the specific activities a
person does, procedures to accomplish them, frequency, and
duration. Stated briefly, these features are what, how, how
often, and how long. Together, frequency and duration
determine the amount of time spent on an activity in a day or
a year.
This analysis examines time spent in 14 activity domains
that span obligatory, committed, and discretionary activities. Data are from the Baltimore Longitudinal Study of
Aging (BLSA) conducted since 1958 by the federal government. We analyze cross-sectional age differences, longitudinal changes for individuals, and secular trends over recent
decades in self-reported activities for men and women of all
adult ages. Do age differences mirror age changes? Have
overall shifts occurred in recent decades in how men and
women spend their time? How socially enduring are age
differences and gender differences in activities?
Background
Cross-sectional age differences in activity patterns have
been studied in sociology and gerontology (Altergott, 1988;
Baltes, Wahl, and Schmid-Furstoss, 1990; Chapin, 1974;
Herzog et al., 1989; Hill, 1985; Juster, 1985a; Lawton,
Moss, and Fulcomer, 1986-87; Moss and Lawton, 1982;
Robinson, 1985a, 1988). In sociology, the strongest tradition of activity research is in time-use (time budget) studies
(Juster and Stafford, 1985, 1991;Szalai, 1972). Subjects are
asked to report all activities for a 24-hour period with start
and stop times, either retrospectively by an interview about
"yesterday" or prospectively by keeping a diary. Before the
1970s, samples were often limited to ages 18-64, thus
excluding older persons; this has changed so there is now no
upper age bound. Subjects' data are aggregated for analyses.
S30
Participation rates (percentages doing an activity) and minutes per day (average minutes for an activity) for subgroups,
such as gender or employment status, are reported. Age
differences were of little interest until the past decade, and
some major studies do not report them (Chapin, 1974;
Szalai, 1972).
Although gerontology studies have a deeper descriptive
and theoretical interest in age differences, their perspective
on activities is often limited. (1) Studies tend to focus on
selected specific domains such as leisure, or ADLs and
IADLs, or productive activities (Altergott, 1988; Gordon
and Gaitz, 1976; Herzog et al., 1989). Until recently,
virtually all research on older persons' activities was about
leisure (Burrus-Bammel and Bammel, 1985; Cutler and
Hendricks, 1990; Gordon and Gaitz, 1976; Kleemeier,
1961). From the 1980s onward, interest in disability
prompted a focus on personal care (ADLs) and household
management (IADLs) activities. Recently, research on paid
and unpaid productive activities (e.g., job, housework,
child-care, volunteer work) has developed to offset the
unbalanced view that older persons' lives are devoted to
leisure and self-care. For particularly thoughtful reviews of
activities in late life, see Lawton (1985a, 1985b). (2) Gerontology studies often focus solely on older persons either by
limiting the survey sample to older ages or by choosing older
respondents from a broad dataset for secondary analysis
(Altergott, 1988). (3) Even when a wide age span is used,
results are usually cross-sectional. It is hard to say how well
the age differences reflect age changes, that is, how activities
change as individuals age. Longitudinal (intra-individual)
data on activity changes are sparse, as noted by Cutler and
Hendricks (1990). Longitudinal data that do exist are retrospective or of limited prospective duration; for example,
changes in leisure activities of retired men over 3 years
(Bosse and Ekerdt, 1981; Parnes et al., 1985), time-use
AGING AND ACTIVITIES
changes for married couples over 6 years (Juster, 1985b),
and changes in leisure activities of older men and women
over 7 years (Schmitz-Scherzer, 1976). Although the Duke
Longitudinal Study had a long prospective stretch of many
years, changes in only a few activities were studied: paid job
(retirement), social leisure (socializing, entertaining, voluntary associations, etc.), and sexual behavior (Palmore,
1981).
Rarely are sociological and gerontological interests combined (Little, 1984). With the BLSA data, we can join
sociology's expansive content (all activities) and comparative perspective (age differences) with gerontology's theoretical interest in the aging process (age changes). We study
age differences and age changes in all activities ranging from
hobbies to hygiene for persons ages 18 + .
The Baltimore Longitudinal Study of Aging
The raison-d'etre of the BLSA is to reveal aging processes
in humans, that is, biological and physiological changes that
are not disease-related (Shock et al., 1984). The central goal
is to portray natural aging rather than normative aging,
which includes deleterious impacts of environmental and
personal risk factors. This distinction is also called successful vs usual aging (Rowe and Kahn, 1987) or, alternatively,
aging processes vs aging syndrome (Fozard, Metter, and
Brant, 1990). The open-panel design of BLSA makes for
both analytic complexity and scientific opportunity. It gives
analysts the chance to identify age changes in many cohorts,
and it routinely permits comparisons of cross-sectional differences with longitudinal changes.
Studies from BLSA have shown that the longitudinal
course of physiological parameters can differ from that
suggested in cross-sectional data (e.g., Brant and Fozard,
1990; Fozard, Metter, and Brant, 1990; Gittings and Fozard,
1986; Hallfrisch et al., 1988). Here, we use social parameters in BLSA and compare cross-sectional and longitudinal
patterns. The issue of natural vs normative aging is not
germane for social data; no matter what time perspective is
used, societal and psychosocial factors penetrate them. Instead, the goal is to study the social experience of aging in a
particular population, attentive at all points to social forces.
METHODS
This section describes the data source and sample, survey
questionnaire and analysis variables, and technical
procedures.
Sample
Data were obtained from an activity questionnaire filled
out by participants in the BLSA, an open-panel longitudinal
study of community-dwelling adult volunteers (Shock et al.,
1984). Men first entered the study in 1958 and women in
1978. Until the 1980s, participants were largely White
upper-middle-class persons; efforts then began to recruit
more non-White and blue collar persons. The current design
calls for a minimum of 30 active participants in every age
decade (<20, 20-29, . . . , 80-89; no minimum specified
for 90 + ) at any given time, for both men and, women. The
BLSA participants have a series of examinations and questionnaires every two years. BLSA is currently conducted by
S31
the Gerontology Research Center, National Institute on
Aging.
The activity questionnaire is administered at every visit. It
was used sporadically from 1958 through 1965; regular
administration began in 1966 and has continued since then.
Data for all questionnaires from 1958 through March 1992
are studied here. There are 8,572 questionnaires available
for 1,816 individuals (1,249 males, 567 females). Men had
an average of 5.5 questionnaires (range 1-19); women had
3.0 (range 1-9). At their first BLSA visit, men's average age
was 53.1 (range 18-96); women's was 52.6 (17-94).
Questionnaire and Variables
Activities. — The questionnaire, called Activity Questionnaire II, asks the amount of time spent on 108 activities
at home, job, and recreation. Subjects give time estimates in
any units (hours per day, days per year, etc.). These are
converted into a standard metric (minutes per day) during
coding. The questionnaire's initial purpose was to provide
estimates of calories expended per day (McGandy et al.,
1966); the items range from physically passive to active
pursuits.
The 108 items cover a broad scope of activities. Using
classification schemes developed for time-use research
(Chapin, 1974; Juster and Stafford, 1985; Szalai, 1972), we
organized them into 14 major domains: personal care, sleep
and resting, walking, local transportation and distant trips,
paid work, housework and food preparation, household repairs and yard maintenance, shopping and errands, child-care
and elder-care, socializing with friends and relatives, entertainment away from home, public service/religious/club/
adult education activities, hobbies and other leisure, and
active sports and physical recreation. They are abbreviated
hereafter as Personal Care, Sleep and Rest, Walking, Transportation, Paid Work, Housework, Repairs and Yard, Shopping, Child-care, Socializing, Entertainment, Public Service,
Hobbies and Leisure, and Sports. A list of the 108 specific
items and their major domains is available on request.
The domains are organized in three overarching types of
activity: Obligatory, Committed, Discretionary. These
terms distinguish among activities required for survival and
self-sufficiency (Personal Care, Sleep and Rest, Walking,
Transportation), principal productive roles and household
management activities (Paid Work, Housework, Repairs and
Yard, Shopping, Child-care), and free-time pursuits (Socializing, Entertainment, Public Service, Hobbies and Leisure,
Sports). Because mobility is essential for getting to roles and
pursuits, it is placed in the obligatory category.
The three terms are similar but not identical to those used
elsewhere (Aas, 1978; Altergott, 1988; Chapin, 1974; Juster
and Stafford, 1985; Moss and Lawton, 1982). In truth,
activities are not so wholly obligatory, committed, or discretionary as the labels suggest (Aas, 1978; Chapin, 1974;
Lawton, 1982). Why an individual does an activity may be a
mixture of constraint and choice, and different individuals
can vary greatly in that mixture.
The BLSA activity data differ from time-use data. For the
latter, respondents record all activities during a day; these
are coded by a comprehensive classification of human activi-
S32
VERBRUGGE ET AL.
ties; each person's minutes add up to 24 hours. For BLSA,
the list of activities is extensive but not comprehensive of all
human activities; minutes do not add to 24 hours. Time-use
researchers call this a stylized protocol (Juster, 1985c).
Empirical comparisons of stylized vs diary formats are in
Robinson (1985b). Two other formats for activity information have been used: a checklist indicating participation in
various activities (Aas, 1978) and direct observation and
recording of activities (Baltes, 1988; Bakes, Wahl, and
Schmid-Furstoss, 1990; Clark, Czaja, and Weber, 1990;
Czaja, Weber, and Nair, 1993).
Dependent variables. — Descriptive analyses discuss
participation rates (percent with ^ 1 minute per day), and
average minutes per day (for all persons and for just participants) by age and gender subgroups for the 14 activity
domains. For multivariate analyses, minutes per day for
individuals are used for the 14 domains.
Predictors. — We study variations in activities by age,
gender, and date. Age is exact age at a BLSA visit (years
plus proportion of year expressed in first decimal). Gender is
a dummy variable with men = 1 and women = 0. Date is
exact date of a BLSA visit (year plus proportion of year
expressed in first decimal). In descriptive results, age is
collapsed to age decades (<20, 20-29, . . . , 80-89, 90 + ) ,
and date is collapsed to time decades (1960s, . . . , 1990s).
Procedures
To accomplish each of the three types of analysis (crosssectional, longitudinal, secular), we chose appropriate portions of the whole data set: (1) The cross-sectional sample
uses each individual's first questionnaire; this is usually from
his/her first BLSA visit. Because each subject is represented
just once in the cross-sectional sample, problems that would
arise in a pooled sample of all visits are absent (e.g., unequal
visits per individual; possible training effects in filling out
the questionnaire). The sample is not cross-sectional in the
usual sense, where data come from a single time-point;
BLSA first questionnaires span 1958 to 1992. (2) For longitudinal analyses of individual change, we use subjects with
four or more activity questionnaires; thus, 4 -I- BLSA visits.
This is 57 percent of the men (n = 715) but only 34 percent
of the women (n = 195) due to their later start in 1978. (3)
For secular trends over decades (1960s, . . . , 1990s), the
analysis sample is constructed so subjects contribute data
just once to an age decade-time decade pair. Operationally,
for a given time decade, a person's earliest visit in an age
decade (such as 30-39) is chosen; if s/he advances to another
age decade within the same time period, s/he can contribute
there as well. For example, for the 1970s time decade, a man
age 47 at a 1972 BLSA visit provides data for age decade
40-49 and then again when he is age 51 in 1976 for age
decade 50-59. Thus, a person can contribute to multiple age
and time decades, but only once in any particular age
decade-time decade pair. Altogether, there are 3,465 data
points provided by men and 1,069 by women for the secular
analysis. Table 1 shows details of the three analysis samples.
Analyses proceeded as follows: First, to evaluate the
variability of activities within and among people, we com-
puted correlations of time spent across the activity domains,
standard deviations of time spent, and correlations across
visits. Second, cross-sectional differences in participation
rates and in average minutes per day were plotted by age for
men and women separately. Regressions of individuals'
minutes-per-day were estimated with predictors for age,
gender, date, and several interactions. Third, longitudinal
trajectories were analyzed and plotted based on two-stage
regressions that yield average changes for age-gender groups
and predict individual changes. Lastly, for secular trends,
average minutes per day were computed for age decades in
each time decade (1960s to 1990s for men, 1970s to 1990s
for women). These were plotted to show changes in activity
levels and age patterns over historical time. Further details
about procedures are stated in Results and Tables as needed.
RESULTS
There are four sections of results: variability of activities,
cross-sectional age differences, longitudinal age changes,
and secular changes across time periods.
Variability of Activities
We study variability of activities across domains, across
persons, and within persons over time. To do so, we analyze
correlations across domains, between- and within-person
diversity, and stability from one time point (visit) to the
next, respectively.
Domains. — Time spent in one activity has little association with time spent in others. Most of the correlations
among the activity domains are < .20 (not shown; available
on request). Only 12 of 91 correlations are ^ .20: Walking is
positively associated with Housework, Repairs and Yard,
Child-care, and Socializing; Housework with Shopping;
Paid Work with Transportation and Public Services; and
Hobbies with Socializing. Paid Work is negatively related to
Housework, Shopping, Sleep, and Hobbies. The underlying
data are not entirely independent; activities that take much
time, such as Paid Work and Sleep, are likely to be negatively correlated. Still, Paid Work is the only activity that
occasions systematic shifts in other activities.
Persons. — Table 2 compares variation in time spent
across persons to variation within persons. The crosssectional data (left side) show average minutes per day for
each activity, standard deviations of means (SD), and coefficients of variation (c.v.; also known as relative standard
errors) measured by the ratio of SD to Y. The longitudinal
data (right side) are average of individual-level Y's, average
of individual-level SDs over time, and average of individuallevel c.v.s. Coefficients of variation permit comparisons
across outcomes whose means differ greatly.
Comparing standard deviations, within-person variation
in minutes spent on an activity is much lower than that found
between persons, often by a factor of two or more. Thus,
individuals have consistency — a certain degree of stability
— in their activities over time.
Comparing coefficients of variation, we can see activities
that vary the most across people and also within individuals.
Cross-sectional c.v.s often exceed 1.00, indicating great
AGING AND ACTIVITIES
S33
Table 1. Questionnaires and Subjects in the BLSA Activities Study
Number of Activity Questionnaires
Questionnaires
Men
Women
Total
Persons with n Questionnaires:
Range
1+
2+
3+
4+
1-19
1249
1018
411
1429
848
303
1151
720
197
917
No.
Mean
6867
1705
8572
5.5
3.0
1-9
4.7
1-19
567
1816
Cross-sectional Sample (No. of Persons; First Questionnaire)
Age:
18-19
20-29
30-39
40-49
50-59
60-69
70-79
80-89
90 +
Total
5
3
8
164
92
256
188
98
286
187
54
241
213
81
294
202
103
305
213
107
320
73
24
97
3
5
8
1249
567
1816
Total
Men
Women
Total
Longitudinal Sample (No. of Persons with 4 + Questionnaires)
Age:
18-19
Men
Women
Total
20-29
3
0
3
81
10
91
30-39
40-49
50-59
60-69
70-79
80-89
90 +
119
38
157
152
25
177
158
47
205
105
51
156
85
23
108
11
1
12
1
0
1
715
195
910
Secular Sample (No. of Persons)
Age:
Men
1960s
1970s
1980s
1990s
Women
1960s
1970s
1980s
1990s
20-29
30-39
40-49
50-59
60-69
70-79
80-89
Total
23
93
62
198
143
24
152
195
125
237
245
107
116
196
57
183
267
184
48
91
26
79
124
57
687
1265
1111
402
—
12
—
—
—
—
—
—
0
46
20
27
46
15
2
68
107
152
40
25
92
33
122
28
88
31
168
669
31
47
37
232
56
18
151
208
Note: Plots for cross-sectional, longitudinal, and secular samples exclude age groups with small n's (18-19, 90 + ; longitudinal plots also exclude 80-89).
In regression analyses, all ages are used.
Table 2. Cross-sectional and Longitudinal Variations in Time Spent on Activities
Longitudinal
(Within Individuals)1"
Cross-sectional
(Between Individuals)"
Y
SD
c.v.
Average
Y
Average
SD
Average
c.v.
Obligatory
Personal Care
Sleep and Rest
Walking
Transportation
144.9
475.9
105.5
63.7
56.4
83.5
115.4
51.6
0.39
0.18
1.09
0.81
140.1
473.3
102.6
62.0
33.5
46.3
66.2
29.8
0.24
0.09
0.70
0.56
Committed
Paid Work
Housework
Repairs and Yard
Shopping
Child-care
222.8
60.2
46.5
11.1
24.1
177.7
73.5
86.5
15.8
70.0
0.80
1.22
1.86
1.42
2.90
199.0
60.8
44.6
11.5
22.9
71.2
30.1
32.9
7.8
23.3
0.87
0.80
0.84
0.88
1.47
Discretionary
Socializing
Entertainment
Public Service
Hobbies and Leisure
Sports
164.4
10.3
12.5
245.3
40.1
125.1
27.7
16.3
133.8
49.5
0.76
2.69
1.30
0.55
1.23
152.7
9.5
12.2
242.4
36.1
69.6
8.2
9.3
55.3
23.9
0.47
1.06
1.02
0.37
0.84
"Based on First Questionnaire data (n = 1816). The c.v. = SD/mean.
"Based on persons with 2 + questionnaires (n = 1429). For each person, the mean and standard deviation are computed for their n visits. Shown are the
average mean (average of all individual Y's), average SD (average of all individual SDs) and the average c.v. (average of all individual c.v.s = individual SD/
individual mean). A c.v. value of 1.00 means the person's value often doubles or halves from one visit to the next.
S34
VERBRUGGE ET AL.
diversity across people, especially for time spent on Repairs
and Yard, Child-care, and Entertainment. The greatest similarity among people occurs for time in Personal Care, Sleep
and Rest, and Hobbies and Leisure. Longitudinal c.v.s are
usually less than 1.00, indicating only moderate degrees of
change over time for individuals. Least variable over adult
years are Sleep and Rest and Personal Care. Most variable
are Child-care, Entertainment, and Public Service.
Visits. — Another way to measure within-person variation is by correlations of minutes spent from one visit to the
next. Longitudinal correlations were calculated for minutes
reported at Visits 1 and 2 and at other pairs of points (not
shown; available on request). There is moderate to high
stability in the over-time correlations; almost all values are
in the range .35-.50. Paid Work and Housework have particularly high stability over time (r = .81 and r = .74,
respectively, for Visits 1 and 2).
Summing up the section. — Individuals differ more from
each other in activities at a given time than they do at various
ages over their adult lives. This does not mean that activities
are static for individuals. Instead, the data suggest that
changes are usually gradual, not precipitous; there is moderate stability in activities from one visit to the next.
Age Differences in Activities (Cross-sectional)
Participation Rates
Rates of participation in activity domains for men and
women are shown in Table 3 (left side).
Gender differences. — Obligatory activities (Personal
Care, Sleep and Rest) have universal rates of participation for
both genders. Mobility activities (Walking, Transportation)
have very high participation rates, around 85-92 percent for
both genders. Committed activities have lowest participation
rates and largest gender differences: Men are more likely to
do Paid Work (74% vs 50%) and Repairs and Yard (87% vs
76%). By contrast, women are more likely to do Housework
(98% vs 68%) and Shopping (91 % vs 60%). For discretionary
activities, Socializing and Hobbies and Leisure are virtually
universal. Other discretionary activities show moderate to
high participation, and sex differences are small.
Age differences. — Many committed and discretionary
activities have a curvilinear pattern with peak participation
rates at ages 30-49, and lower participation in young and old
adult years (not shown; available on request). For men, such
crests appear in Walking, Paid Work, Repairs and Yard,
Child-care, and Public Service; and for women, in Transportation, Repairs and Yard, Shopping, Child-care, Public
Service, and Sports. The drops in participation after midlife
are most pronounced for Paid Work, Child-care, Public
Service, and Sports for both genders. There is just one
instance of a positive association between participation and
age (that is, percents rise across age groups); it is Shopping
by men. Overall, the data suggest a narrower repertoire of
activities for older persons, that is, less diversity. This has
been shown elsewhere (Altergott, 1990) for the variety of
people with whom one spends time.
Table 3. Cross-sectional and Longitudinal Participation Rates
Longitudinal6
Cross-sectional"
% doing activity
"now"
Men
Obligatory
Personal Care
Sleep and Rest
Walking
Transportation
% doing activity
"ever"
Women
Men
% doing activity
"always"
Women
Men
Women
100.0
100.0
90.7
91.7
100.0
100.0
91.0
85.3
100.0
100.0
97.4
95.2
100.0
100.0
96.5
89.9
100.0
100.0
73.0
78.1
100.0
100.0
81.4
78.3
Committed
Paid Work
Housework
Repairs and Yard
Shopping
Child-care
74.0
67.5
86.7
60.0
40.9
49.9
98.0
75.8
90.8
39.9
78.6
88.5
93.2
83.6
60.3
57.1
98.8
85.4
94.9
55.7
46.5
47.9
70.3
35.4
15.2
38.6
95.9
61.4
80.4
23.5
Discretionary
Socializing
Entertainment
Public Service
Hobbies and Leisure
Sports
97.8
67.3
67.1
99.6
87.0
98.6
77.2
56.4
99.6
83.9
99.2
84.3
85.2
99.9
93.9
99.7
85.5
74.2
100.0
90.3
93.3
35.4
35.6
98.7
65.1
97.5
60.5
38.3
99.3
69.8
"Based on First Questionnaire data (n = 1816). "Now" means reported at first questionnaire. Persons with missing data for an activity are scored 0 (except
for Personal Care and Sleep and Rest: persons with missing data are excluded; thus, percents equal 100.0).
b
Based on all persons with 1 + visit (n = 1816); see Table 1 for distribution of no. of visits. "Ever" means reported at any questionnaire; "always" means
reported at all questionnaires.
AGING AND ACTIVITIES
Cross-Sectional Differences in Time Spent
We evaluate the frequency distributions of minutes per
day for the 14 domains. Then we discuss plots of average
time spent by men and women of different ages. Lastly, we
estimate regressions of individuals' minutes on age, gender,
and date.
Frequency distributions. — Distributions were generated
for the cross-sectional sample (n = 1,816), all questionnaires (N = 8,572), and individual means (n = 1,816);
results are the same. Several activity domains have normal
distributions: Personal Care, Sleep and Rest, and Hobbies
and Leisure. Truncated normal distributions (some percent
of nonparticipants at 0, larger percents with low-to-moderate
minutes, and percents tailing off for high minutes) appear for
Transportation and Socializing. Paid Work has a bimodal
distribution with peaks at 0 and about 350 minutes (6 hours).
The other domains all show reverse J shapes (substantial
percents of nonparticipants, and participants tailing out to
the right in decreasing percents): Walking, Housework,
Repairs and Yard, Shopping, Child-care, Entertainment,
Public Service, and Sports. Despite this variety, we study
minutes-per-day without transformation so results for all
domains can be compared with each other.
S35
A. Obligatory
- Personal Care-Men
600 -r
- Personal Care-Women
- Sleep & Rest-Men
- Sleep & Rest-Women
- Walking-Men
- Walking-Women
- Transportation-Men
- Transportation-Women
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Age Decades
B. Committed
- • - Paid Work-Men
400 -i
- o - Paid Work-Women
^
350 •
— • — Housework-Men
S 300 - « — Housework-Women
1 2501 200 -
—*— Repairs & Yard-Men
^
— A — Repairs & Yard-Woman
150 -
CO
|
100 -
-•—Shopping-Men
50 -
- o — Shopping-Women
- x - Childcare-Man
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Plots of cross-sectional means. — Average minutes-perday for age decades (20-29, . . . , 80-89) were computed
using first questionnaires. Patterns for obligatory, committed, and discretionary activities are in Figure 1, Panels A-C,
for both genders. The means include all persons (participants
and nonparticipants, who score 0); results for just participants show the same patterns.
Gender Differences: For the most essential activities (Personal Care, Sleep and Rest), gender differences are small at
all ages. By contrast, they are very evident for committed
activities and mobility: Men spend more time in Paid Work,
Repairs and Yard, and Transportation, and women more
time in Housework, Shopping, Child-care, and Walking.
For discretionary activities, men spend more time in active
pursuits (Sports), and women more time in passive ones
(Socializing until late ages, and Hobbies and Leisure at most
ages). Both genders spend only small amounts of time on
Entertainment and Public Service at all ages.
Age Differences: (1) Several activities have the most
minutes in late life: Obligatory activities (Personal Care,
Sleep and Rest) have steady time spent up to midlife (age 50)
and greater after that. Shopping, Hobbies and Leisure, and
(for men) Housework times decrease from young to middle
ages, then rise at older ages. (2) For most other activities,
minutes per day decrease across adult ages (Transportation,
Socializing, Entertainment, Sports for both genders, Walking for men, Paid Work for women) or after a midlife crest
(Child-care, Public Service for both genders, Paid Work for
men, Housework for women). (3) Roughly equal time is
spent on Repairs and Yard at all ages.
Ranking the times spent in the 14 domains within each
age-gender group, we find some commonalities: Sleep and
Rest occupies the greatest number of minutes per day at all
ages, for both genders. Paid work ranks next for men until
about age 60 and for women until about age 40; then Hobbies
— « — Childcaro-Women
Age Decades
C. Discretionary
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Age Decades
Figure 1. Cross-sectional activity means by age decade and gender.
A. Obligatory activity means; B. Committed activity means; C. Discretionary activity means.
and Leisure move from third into second rank for both.
Socializing typically ranks fourth, followed by Personal
Care and Walking. Of the 14 domains, the least time is spent
on Public Service, Shopping, and Entertainment. Most activities retain their rank across age decades; that is, their
prominence (high or low) in activity profiles is the same at
every age. A few activities show striking drops in prominence for both genders (Paid Work, Child-care), and a few
show clear rises in prominence for both genders (Personal
Care, Housework, Shopping, Hobbies) for both genders.
Cross-sectional regressions. — Using individual data, we
computed regressions with predictors for age (also age2,
age3), gender, date, and interactions among them. Several
models were estimated, the simplest with just main effects
VERBRUGGE ETAL.
(age, gender, date; abbreviated as A, G, D) and the most
complex with main, interaction, and age-squared and -cubed
terms. For a given activity, all models were estimated and
then evaluated for fit, starting at the most complex model
and working toward smaller ones. Models with nonsignificant higher-order interactions were dropped. The best-fit
model is the smallest one with significant interactions or age"
terms; if they are all nonsignificant, the main-effects model
is taken as the best fit. The regressions include participants
and nonparticipants (0 minutes); regressions estimated for
participants show virtually identical results. Table 4 presents
results for the best-fit models.
We discuss the best-fit models for each activity domain,
starting with simplest results:
Sleep and Rest, Walking, Transportation, and Public Service are predicted with the main-effects model. Age is positively related to Sleep and Rest, and negatively related to the
other domains. Men engage in less Walking and slightly less
Sleep and Rest, but more Transportation, than do women.
Public Service has declined from the 1960s to 1990s. R2s are
small for these models, ranging from .025-.088.
Housework, Shopping, and Socializing have important
two-way interactions. Housework (R2 = .413) shows a
slight increase with age, less time by men, and small decrease in recent decades. But that is too simplistic, and
interactions indicate that older men and women are more
similar in housework time than at younger ages (A*G) and
that men's housework time has increased in recent decades
while women's time has decreased (D*G). Socializing (R2
= .119) has increased in recent decades, especially for
women and young adults. Shopping (R2 = .101) shows
more time by women and recent rises in men's involvement.
(Entertainment also has two-way interactions but all effects
are small; R2 = .017. Personal Care has higher-order age
terms but all effects are small; R2 = .065.)
Child-care (R2 = .115) is still more complex, having
more interaction terms. Women's greater involvement is the
principal effect. Overall decline in Child-care over time is
also indicated, but changes have been highly variable for
age-gender groups.
Paid Work, Sports, Repairs and Yard, and Hobbies and
Leisure have the most complex models. For Paid Work (R2 =
.538), age, gender, and date operate jointly, not on their own.
Main effects vanish into strong interactions: older men and
women are more similar in worktime than younger adults
(A*G), an obvious reflection of higher male employment at
young and middle ages followed by their sharper retirement
patterns. Men and women are now more similar in worktime
than in prior decades (D*G), as men retire earlier while
middle-aged women obtain employment. Further, declines in
employment at ages 50-64 have become sharper than in prior
decades (A*D). The age-squared term (A2) reflects the curvilinear pattern of employment with peak rates in middle ages.
Significant interactions of a more complex nature (A2*G,
A2*D, A3*G, A3*D) also show the close social nexus of age,
gender, and date in affecting paid work. For Sports (R2 =
.130), regressions indicate secular increases of differing
amounts in age-gender groups. For Repairs and Yard (R2 =
.053), the complex age-gender-date effects are hard to interpret and not revealed by plots. For Hobbies and Leisure (R2
= .090), effects are complex but small.
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AGING AND ACTIVITIES
Age Changes in Activities (Longitudinal)
Participation Rates
Using all questionnaires for each person, we created two
longitudinal participation rates: percent who ever participate
in an activity (reported at any BLSA visit) and percent who
always participate (reported at all BLSA visits). Results are
in Table 3 (right side).
Ever participation. —Not surprisingly, "ever" participation rates are uniformly higher than the cross-sectional
("now") rates. The sharpest rises are for Housework and
Shopping by men; this means men do the activities at some
point(s) in adult life but irregularly. There are also sizable
rises in ever participation for Child-care and Public Service
for both genders, compared to cross-sectional (based on just
one visit). Gender differences for ever rates are the same as
noted for cross-sectional rates, but the differences are
smaller because ever rates are so much higher.
Always participation. — Universal activities (Personal
Care, Sleep and Rest, Socializing, Hobbies and Leisure)
noted in cross-sectional data are also invariant ones; people
are doing them at every questionnaire. Otherwise, "always"
rates are uniformly lower than cross-sectional ("now") rates.
Men show many large differences (Walking, Paid Work,
Housework, Shopping, Child-care, Entertainment, Public
Service, Sports), reflecting sharper role changes they experience over life. By contrast, women show just one sizable
reduction (Public Service) for always vs now.
In sum, the data show that women are more consistent, or
steady, than men in their activities over time. This is confirmed by ratios of always to ever participation and ratios of
always to now (not shown). The ratios are almost always
higher for women than for men, especially for Housework
and Shopping.
Longitudinal Trajectories of Time Spent
We estimate and plot longitudinal trajectories of minutes
per day from young to late adulthood. These age changes are
compared with cross-sectional age differences.
Regressions of trajectories. — Using all subjects with 4 +
visits, we computed regressions for each individual of the
form: MinuteSi = f[Timey, Time^2]. The subscript i identifies
the person; the subscript j identifies the BLSA visit. Time is
years since first visit to Visit j (number of years plus
proportion of year expressed in first decimal). This adjusts
precisely for the time between adjacent visits whether they
occurred on schedule (two years) or had larger gaps. Each
person's regression produces an intercept (b0) and two slopes
(b,, b2). Collecting these across individuals, we have three
sets of regression parameters for every activity domain.
Then second-stage longitudinal regressions were produced. The sets of individual-level intercept (b0) and slope
(b,, b2) values were regressed on age (also age2, age3),
gender, date, and interactions among them. Age and date
refer to first questionnaire. The models and procedures were
the same as for cross-sectional analyses. The longitudinal
regressions were also estimated for participants (at least one
S37
non-zero value in their 4 + visits); final results are virtually
identical to all persons. The second-stage regressions give
information about initial levels and paces of change as
follows: Regressions for b0 are substantively parallel to the
cross-sectional regressions because the individual intercepts
estimate minutes "at the beginning" (first visit). Regressions for b, and b2 show factors that affect paces of change.
Coefficients in the slope regressions represent interactions
with Time and Time-squared; for example, if a significant
Age effect occurs for b,, it actually indicates an Age X Time
interaction. A table showing results from best-fit models for
b0, b,, and b2 is available on request.
Initial Levels: In most cases, the best-fit model is the same
or close for the b0 regressions and the cross-sectional regressions. Specific results (significance and signs of coefficients)
are sometimes similar but not as much as anticipated; R2s
tend to be lower and predictor effects weaker for the longitudinal results.
Paces of Change: The b, and b2 regressions have low R2
values (all =s .10). Predictor coefficients are seldom significant; thus, differences in paces of change that individuals
experience are not closely related to their entry age, gender,
or entry date. Effects are hard to interpret in specific ways
(noted above) and we chose not to do so. This does not
vitiate the utility and necessity of b, and b2 for estimating and
plotting longitudinal trajectories — which we will now
present.
Plots of trajectories. — To generate plots of age changes,
we grouped the individual-level intercepts and slopes by age
decade (20-29, . . . , 70-79) at first questionnaire. Subjects
with ages < 20 or 80+ were dropped because of small n;
however, the small longitudinal sample of women 20-29
was retained for comparison with men those ages (Table 1).
Within age decade, average values for the intercepts and
slopes were calculated. Using these, minutes per day were
estimated for the midpoint age, 5 years later, and 10 years
later. For example, the 20-29 group has estimates for ages
25, 30, and 35. Thus, each age decade produces a 10-year
forward trajectory. This is a highly conservative use of the
longitudinal data in two respects: It does not extrapolate
beyond most subjects' actual measurements (on average, the
men had 15 years of follow-up since first visit, and women
10 years), and it uses each person's longitudinal data just
once, the age decade of first questionnaire.
The longitudinal estimates were plotted for the 14 activity
domains. Cross-sectional means were included on the plots
for comparison. (Cross-sectional means are placed at midpoints such as 25, 35, etc.; values for 30, 40, etc. are
extrapolations between observed values.)
The largest differences between longitudinal and crosssectional patterns appear for Paid Work, Housework, and
Sleep and Rest (Figure 2, Panels A-C). (Technical note: For
Paid Work, there is no longitudinal line for women 70-79
because none had paid jobs.)
For Paid Work, the two patterns match closely for men —
but not for women. Whereas cross-sectional patterns for
women show a steady decline across adult ages, the longitudinal patterns show pronounced increases in minutes working from ages 35 to 44, 45 to 54, and even 70 to 75. These
S38
VERBRUGGE ET AL.
A. Paid Work
30 35 40 45 50 55 60 65 70 75 80 85
25
Patterns for Housework are in the opposite direction from
Paid Work. For men, cross-sectional and longitudinal results
both show a gradual increase in the amount of time spent in
housework with age. This is especially pronounced in the
longitudinal series, indicating rising contributions by recent
cohorts of men. For women, cross-sectional data show increasing housework time from young adulthood through
midlife with declines thereafter. But longitudinal data show
sharp decreases in housework for women under age 55.
Presumably, the reason lies again in secular shifts in women's
employment,, with job-time supplanting housework.
For Sleep and Rest, there is strong correspondence between the cross-sectional and longitudinal data for men, but
some differences appear for women. Their cross-sectional
data show small linear increases in Sleep and Rest after
about age 60. Longitudinal data, however, show declines in
minutes of Sleep and Rest from age 35 onward, especially
after age 65. The longitudinal results tally with clinical
knowledge about sleep decreases in later life.
For the other 11 domains, longitudinal patterns repeat the
cross-sectional ones closely (plots available on request).
Although succinctly stated, this is an extremely important
result. It means that for most activities, cross-sectional
results offer a very good clue about what will happen as
individuals age.
Secular Changes in Activities
25
30 35 40 45 50 55 60 65 70 75 80 85
C. Sleep & Rest
550
T
500--
450 -•
400 -
350 -
300
25
30 35 40 45 50 55 60 65 70 75 80 85
Figure 2. Cross-sectional and longitudinal mean minutes per day. A. Paid
work; B. Housework; C. Sleep and rest. Symbols: • Cross-sectional men;
• Cross-sectional women; • Longitudinal men; and o Longitudinal women.
findings portray real experiences as women ages 30-39,4049, and 60-69 aged 10 years. They undoubtedly reflect
women's increasing employment in recent decades, especially during child-raising years and shortly after (Taeuber,
1991).
The cross-sectional and longitudinal activity patterns occur within a changing sociocultural context. From the 1960s
to the 1990s, American society expanded employment and
social opportunities for women and older persons, and it
tolerated more diversity among people in their life dreams
and daily pursuits. Historical effects penetrate BLSA data,
certainly in social outcomes but possibly in physiological
ones too. Such effects can be singular and brief (prompted
by war or catastrophe) or secular and gradual (prompted by
slow societal changes); our interest is the latter.
Several results above have suggested secular changes in
time spent on activities, and we now look at them directly
across the 1960s, 1970s, 1980s, and 1990s. For each decade, mean values for age groups were plotted. As noted
earlier, a person contributes just once to a given mean but
can contribute to multiple ones as s/he moves to other age
decades and time decades. Plots for Paid Work, Housework,
Socializing, and Personal Care are shown in Figure 3, Panels
A-D; others are available on request.
Committed activities show definite secular changes. In all
time decades, Paid Work (Figure 3A) by men begins to fall
around ages 50-59. The drop has become steeper over time
because of trends toward earlier retirement (Hayward,
Grady, and McLaughlin, 1988a, 1988b; Tuma and Sandefur, 1988). By contrast, Paid Work by women shows sharp
rises in midlife (ages 40-69) over the decades. Housework
(Figure 3B) shows exactly the opposite changes, with rising
involvement by men since the 1960s and declining involvement by women at all ages. Men at most ages have also
increased their time on Repairs and Yard, Shopping, and
(just ages 30-49) Child-care. Women also increased Repairs
AGING AND ACTIVITIES
S39
A. Paid Work
'er Day
400 -I
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as
350 •
300 250 -
-K
200 150 100 -
50 --
20-29 30-39 40-49 50-59 60-69 70-79 80-89
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Age Decades
Age Decades
D. Personal Care
C. Socializing
180
350 T
T
20-29 30-39 40-49 50-59 60-69 70-79 80-89
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Age Decades
Age Decades
Figure 3. Secular trends in mean minutes per day by age and gender groups. A. Paid work; B. Housework; C. Socializing; D. Personal care. Symbols:
• 1960s Men (no data for Women in the 1960s), • 1970s Men, O 1970s Women, • 1980s Men, A 1980s Women, • 1990s Men, and O 1990s Women.
and Yard time at most ages, but the other two activities show
little change.
Discretionary activities are taking more time in men's and
women's lives. Socializing with friends and relatives (Figure 3C) has increased for men of all ages and for women in
young and middle adulthood. In recent decades, men are
spending more time in Hobbies and Leisure and in Sports.
Trends for women are in the same direction, but smaller and
uneven. Entertainment shows slight uneven rises over time
for both genders. In contrast to all other discretionary activities, Public Service shows decreased minutes for young and
middle-aged men and women from the 1960s to 1990s.
Secular changes in obligatory activities are especially
interesting. Personal Care time has decreased in midlife for
both men and women, but risen for young adults (20-29)
and for older women (70 + ) (Figure 3D). Time spent in
Sleep and Rest has decreased at most ages for women, but
there is no clear trend for men. Transportation shows striking
changes: women ages 30-79 are spending more time in local
travel now than in prior decades due to rising employment,
greater independence, driving skills, and access to public
transport. Men these ages also spend more time in Transportation than before, but the trends are smaller. Walking has
increased at young and middle ages (20-49) for men and
women; there is no trend at later ages.
National time-use studies in the 1960s and 1970s showed
the following trends: (1) decreasing worktime among employed persons overall but rising worktime for women; (2)
decreasing family care (includes housework, child-care,
shopping, repairs) by women but increasing time on those
activities by men; (3) sharp increases in total leisure activities (principally in mass media, adult education, and recreation, but declines in certain categories such as visiting and
other informal social life); and (4) no sizable or consistent
changes for personal care or sleep (Juster, 1985b; Robinson,
1985a, 1988; Robinson, Andreyenkov, and Patrushev,
S40
VERBRUGGE ET AL.
1988; trends for earlier decades in Robinson and Converse,
1972). Our data show all of these with the exception of
socializing (secular rises in BLSA). Robinson's well-known
finding of sharp rises in television viewing cannot be
checked in the BLSA data because it is within the item
"reading/watching TV/listening to records."
DISCUSSION
This is the first study of longitudinal and secular changes
in activities that has broad scope for activities and time
period. We compare the longitudinal age changes to crosssectional age differences to assess if they give the same
message about aging.
The longitudinal changes we find are sturdy with respect
to generalizability. First, selective aspects of the BLSA
sample are not very influential; wherever comparisons to
other time-use studies are possible, our results are similar to
them. Second, although historical era may be important in
the long run of a century or so, it is not in the several decades
studied. Even when activity levels (average time spent) shift
during the observation decades, age patterns stay the same.
We chose to study demographic and date effects on
activities, viewing them as premier factors that can never be
ignored. Other social factors (e.g., marital status, parental
status, living arrangements, education) affect people's activities, but the analyses were complex enough with demographic and date factors so we omitted them. Also, we chose
an analytic approach commonly used in human development
research rather than a demographic approach that distinctly
separates age, period, and cohort effects. Ours is excellently
suited to tracing individual as well as group trajectories.
We conclude by stating the most striking results and what
they mean:
1. Between People and Across Life. Individuals differ
more from each other in time spent on activities (crosssectional) than they vary as they age (longitudinal). This
does not mean that people have static activity profiles.
Instead, it means that changes tend to be gradual — giving
both consistency and dynamics to life.
2. Age Differences. Time spent on Personal Care, Sleep
and Rest, Shopping, and Hobbies and Leisure is greater for
older persons than young and middle-aged adults. Thus, the
most essential and universal aspects of living, and also those
with the lowest physical and mental demands, take up a
larger fraction of older adults' days. Activities with the
highest physical and mental requirements (mobility, job and
other committed activities, active leisure) are given less time
by older persons than by other age groups.
3. Age Changes. Longitudinal changes as individuals age
usually mirror cross-sectional age differences — a strong
and important result. But there are a few important exceptions for women. Recently, women have increased their Paid
Work involvement as they grow older, in contrast to crosssectional declines in Paid Work across adult ages. Just the
opposite appears for Housework; young and middle-aged
women show sharp longitudinal declines, compared to
cross-sectional rises at those ages.
4. Secular Trends. In the latter half of the 20th century,
expanded opportunities for women and for older persons
translated directly into activity changes in all domains from
obligatory to leisure. Most striking of all, women increased
Paid Work and decreased Housework, while men decreased
Paid Work and increased Housework. Thus, there is some
convergence of men's and women's main productive activities, particularly in midlife (see also Juster, 1985b). Time
spent on discretionary activities (Socializing, Hobbies and
Leisure, Sports, Entertainment) and on Transportation has
increased. By contrast, time spent on Personal Care and Sleep
and Rest has decreased, especially in midlife and for women.
Age, gender, and date all play strong parts in the crosssectional, longitudinal, and secular results. All three are
powerful forces for stasis and change in activities.
The most powerful force for stasis? The empirical hat tips
toward age. Patterns of age differences have been very steady
across time decades, even though levels (minutes per day)
have shifted (Figures 3A-3D). Thus, the age structure of
roles and activities is strongly embedded in our society so that
age differences persist in the midst of much social change.
The most powerful force for change? Here, the hat tips
toward gender. Women's longitudinal increases in Paid
Work paired with decreases in Housework constitute a
striking breakthrough in the age structure of roles. If high
employment during childrearing years and after continues,
the age pattern of paid work for women will gradually come
closer to men's longstanding pattern of peak employment at
ages 30-59. Men also changed their age structure of roles,
though less strongly, by longitudinal decreases in Paid Work
and increases in Housework.
As Riley has often noted (1976, 1978, 1988), at all times
there is both strong resistance and momentum for change in
age roles. Where does the balance lie in recent decades? Our
results show that longstanding sociocultural factors related
to age have maintained their force in determining people's
activities, and changes have been of modest scope. Change
arises in the context of great stability, rather than vice versa.
ACKNOWLEDGMENTS
The authors thank colleagues at the Gerontology Research Center (GRC),
National Institute on Aging for advice and assistance, especially Dr. E.
Jeffrey Metter (Medical Officer), Dr. Jay D. Pearson (Statistician), and Ms.
Cathy Dent (Data Management) of the Longitudinal Studies Branch. We
thank the many BLSA subjects who answered the activity questionnaires
and three reviewers who provided excellent suggestions for revision.
Michelle Putnam and Clayton Lawrence assisted in data management and
analysis during summer internships at GRC.
This research was supported by a postdoctoral fellowship to Dr. GruberBaldini (T32 AG00114-07; Institute of Gerontology, University of Michigan) and by GRC internal assistance while the authors worked there.
Preliminary findings were presented at The Gerontological Society of
America Annual Meeting, Washington, DC, 1992.
Address correspondence to Dr. Lois M. Verbrugge, Institute of Gerontology, 300 North Ingalls, The University of Michigan, Ann Arbor, MI
48109-2007.
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Received November 19, 1993
Accepted March 7, 1995