Vol.5, No.2, 179-187 (2013) Health
A new category of “future planning” in the activity
card sort: Continuity versus novelty in old age
Tsameret Ricon*, Pola Weissman, Naor Demeter
Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel;
*Corresponding Author: tricon@univ.haifa.ac.il
Received 30 November 2012; revised 29 December 2012; accepted 6 January 2013
The Activity Card Sort (ACS) is a widely used
measure for assessing participation in instru-
mental, leisure, and social-cultural activities.
The ACS addresses previous and current acti-
vities but not future activity plans. The purpose
of the study was to extend the ACS to include
future planning. Previous research indicates
that participation in activities and future plan-
ning is positively related to life satisfaction, and
increased well-being and that these positive
effects were most pronounced for adults 60
years and older. The current study participants
were 60 Israeli adults aged 55 - 74 years. The
research finds future planning to be widespr ead,
common and significant among older adults.
Moreover, it w as found that older people p lanned
to continue previous activities more that they
planned new activities for the future, indicating
more continuity than innovation among the par-
ticipants in this study. Participants with higher
current or past activity levels planed a greater
number of future activities. Construct validity
using known group method showed the ex-
tended ACS to have discriminant validity with
respect to age (younger participants were more
active) and gender (highly physical activities
were favored by men). MANOVA repeated mea-
sures and Pearson correlations demonstrated
moderate-high test-retest reliability for the ex-
tended ACS.
Keyw ords: Activity Card Sort; Continuity;
Innovation; Older Adults; Reliability & Validity
As life expectancy increases, so does the size of the
elderly population. Older people have more free time and
are more active in comparison with their predecessors [1,
2]. Research has indicated that greater overall activity
level in older adults was related to greater happiness,
better function, and reduced mortality [3]. Moreover,
evidence has shown that activities associated with leisure
and independent living, is a meaningful part of their lives
and participation in occupational areas of performance.
Independent living activities, referred to as instrumental
activities of daily living (IADL), include activities such
as preparing meals or shopping, [4,5]. Leisure activities
include social and productive activities, as well as more
solitary activities (e.g., reading, handwork, hobbies) and
were related to feelings of well-being, function and hap-
piness and to a sense of engagement with life [3]. Con-
sequently, these activities are a primary focus of occupa-
tional therapy (OT) interventions for this population.
The World Health Organization’s International Classi-
fication of Functioning, Disability and Health empha-
sizes the significance of promoting health and well-being
by enabling participation, and defines participation as a
person’s involvement in life situations [6]. However, the
greatest influence on activity patterns is the age of the
participant, with the diversity and frequency of leisure
activities decreasing as participant age increases [7]. This
tendency may be associated with age-related changes to
people’s lifestyle and family structures [4,5].
The concept of “future planning” has been well studied
in the gerontology literature of the last two decades and
shows that most future planning occurs around retire-
ment age [8-11]. Retirement involves marked changes
within a short period of time and is therefore a signifi-
cant transition in life requiring a process of planning [8].
In planning for the post-retirement phase of their lives,
older adults have been found to adopt two main strate-
gies: continuity and innovation. Continuity has been de-
fined as the maintenance of familiar leisure activities into
retirement and is a major characteristic of the post-re-
tirement period [12,13]. Therefore leisure activities are
likely to be continued throughout the ageing process,
despite physical changes resulting from the normal age-
ing process, in order to preserve and maintain existing
individual and social identities [14-18]. Continuity ap-
Copyright © 2013 SciRes. OPEN A CCES S
T. Ricon et al. / Health 5 (2013) 179-187
pears to aid adjustment to old age and is seen as a strat-
egy that promotes coping. Older people seek to develop
activity patterns that are as stable as possible in order to
maintain the social and psychological characteristics they
obtained through life. Continuity serves as an attribute of
successful adaptation to old age and its associated diffi-
culties, such as deteriorating health status, decreased
financial income and narrowing social circles [9,14-19].
The phenomenon of innovation is relatively unex-
plored in the literature. Evidences show that innovation
in old age is less common [20-22], although some evi-
dence exists showing that older adults do tend to inno-
vate in their leisure participation [23]. While preserving
and maintaining existing individual and social identity is
important to well-being during the ageing process, ag-
ing may also provide opportunities for engaging in new
experiences [10,24-26].
In the context of leisure, innovation is defined as an
older adult’s post-retirement participation in at least one
leisure activity in which he or she did not engage prior to
retirement. Innovation in leisure activities may contribute
to a feeling of well-being in old age and is not only about
the unknown new activity, since it also reflects a new
source for a familiar experience [10]. Innovation is more
commonly adopted by women than men [27], and char-
acterizes people who retired from a full time job, who
perceive their health as good, retired out of free will,
have a Western cultural background, and expect a longer-
term retirement [23]. However, few researchers have
focused on innovation and thus far only in a Western
context, such that it is not clear if innovation is also
common elsewhere.
Investigating future planning by older adults in an OT
context requires the use of suitable tools through which
to gather relevant information and create an occupational
profile. This process enables the therapist to establish
intervention goals and outcome measures together with
the client [28]. Many evaluation tools assess occupa-
tional performance [29,30], however very few assess
leisure and IADL activities in an ecological context [31].
Although a few assessment tools assess the leisure and
IADL occupational domains, most are structured as ques-
tionnaires or scales (e.g., COTE [32], COPM [33]). The
uniqueness of the ACS lies in the life-like pictures it
presents to the person to sort. Examples of occupational
therapy evaluation tools for leisure are the Interest
Checklist [34] and the Occupational Questionnaire [35],
while the Lawton and Brody questionnaire offers a
means of assessing the IADL abilities of older adults.
Although these questionnaires are reliable and valid [34-
36], they require the ability to understand written text in
addition to possessing an intact verbal memory. They are
therefore unsuitable for sections of the older adult popu-
lation in whom such skills may have deteriorated.
The Activity Card Sort (ACS) [37,38] evaluates past
and current participation in instrumental, leisure and so-
cial activities. It was designed originally for people with
Alzheimer’s disease [39] and is used today to evaluate
how older adults cope with various diseases or life events.
The ACS has been studied in numerous countries and
was found reliable and valid [40-43]. The ACS involves
sorting pictures of leisure, social-cultural, and IADL ac-
tivities into categories. The visual representation method
is less threatening than completing a form and achieves
better client cooperation [44]. Although it provides valu-
able information on past and present activities, the ACS
does not provide information regarding the activities in
which a person plans to participate in future. A future
planned activity suggests that the client plans to adopt
continuity and/or innovation strategies. The knowledge is
required in order to create a full client activity profile in
an OT intervention and to identify meaningful future
Thus, there is insufficient information in the literature
regarding future planning by older adults. This study
seeks to address this gap by investigating a tool for the
collection of future planning data from older adults in an
occupational therapy setting. The aim of the current
study is to expand the ACS tool by adding a new “Future
Activity” sorting category to the existing “Past Activity”
and “Current Activity” sorting categories in order to shed
light on continuity versus novelty in activities of older
age. Three main research questions were defined: 1) Do
older people plan social and recreational everyday activi-
ties for the future? 2) Are older people more likely to
plan future activities in which they have been engaged in
the past, or new ones? 3) What is the validity and reli-
ability of the extended ACS after adding a new category?
The purpose of this new category is to identify activi-
ties in which the person plans to participate in the near
future and to differentiate between innovation and con-
tinuity in the choice of future activities. The uniqueness
of the current study lies in its focus on planned future
leisure activities among older adults with the objective of
promoting life quality and well-being. The study was
undertaken with the approval of the author of the ACS,
Prof. Carolyn M. Baum.
The study utilized a convenience sample of 60 parti-
cipants, aged 55 - 75 years. The participants live near
Haifa and were recruited from the authors’ acquaintances.
The sample was divided into two age groups used in the
literature [45]: older adults (aged 55 - 64 years) and eld-
erly adults (aged 65 - 74 years). All the participants met
the inclusion criteria (see below), had not been diagnosed
with a mental/physical disability or neurological disease
Copyright © 2013 SciRes. OPEN A CCES S
T. Ricon et al. / Health 5 (2013) 179-187
Copyright © 2013 SciRes. OPEN A CCES S
and possessed normal cognitive abilities. The demo-
graphic characteristics of the sample are presented in
Table 1.
Each age group included a similar number of men and
women. Mean educational attainment level was 14.53
years (SD = 3.29). All the participants were Hebrew
speakers and were independent with respect to activities
of daily living (ADL) and IADL. All participants signed
an informed consent form.
2.1. Instruments and Inclusion Criteria
2.1.1. The Screening Questionnaire
The questionnaire was developed for this research and
consisted of eight questions that asked whether the par-
ticipant was currently diagnosed with a mental/physical
disability or neurological disease. Only participants who
answered “no” to all eight questions were included in the
2.1.2. Mini-Mental State Examination (MMSE)
The MMSE [45] is a common neuropsychological
screening test in field research and clinical practice that
evaluates the general cognitive ability of adults. The
maximal score is 30. A score of 24 - 30 indicates normal
cognitive ability and achievement of this score was the
second requirement for inclusion in this study.
2.1.3. Demographic Questionnaire
This questionnaire was used to collect demographic
information concerning the participants with respect to
gender, income level, residence, highest educational at-
tainment, and place of birth.
2.1.4. Activity Card Sort
The ACS includes 88 activity cards representing rea-
listic pictures of older adults engaged in four activity do-
mains: IADL, social-cultural leisure, low physical leisure,
and high physical leisure. Three versions of the ACS are
available: one for hospitalized older adults, a second for
those in a recovery period, and a third for older adults
living independently in the community. The current
study employed a Hebrew translation of the third version
of the ACS [41,46].
Baum (1995) reported on the construct and content va-
lidity of her original ACS measure among 72 Alzhei-
mer patients in different stages of the disease [37].
Gonen et al. [46] found the measure to have construct
and content validity among healthy older adults in Israel,
and a high significant internal consistency in the IADL
domain (r = 0.83) and in the social-cultural domain (r =
0.80). It has moderate significant reliability in the low
physical leisure (r = 0.66) and high physical leisure (r =
0.61) domains.
Table 1. Demographic characteristics of the sample as numbers and percentages.
(55 - 64 years)
(n = 33) (55%)
(65 - 74 years)
(n = 27) (45%)
(n = 28)
(n = 32)
(n = 60)
Income Level Low 0 4 (14.8%) 2 (7.1%) 2 (6.2%) 4 (6.7%)
Medium 30 (90.9%) 16 (59.3%) 19 (67.9%) 27 (84.4%) 46 (76.7%)
High 3 (9.1%) 7 (25.9%) 7 (25%) 3 (9.4%) 10 (16.7%)
Residence City 23 (69.7%) 20 (74.1%) 19 (67.9%) 24 (75%) 43 (71.7%)
Kibbutz 5 (15.2%) 3 (11.1%) 4 (14.3%) 4 (12.5%) 8 (13.3%)
Village 0 3 (11.1%) 2 (7.1%) 1 (3.1%) 3 (5%)
Other 5 (15.2%) 1 (3.7%) 3 (10.7%) 3 (9.4%) 6 (10%)
Highest Elementary 1 (3%) 4 (14.8%) 3 (10.7%) 2 (6.2%) 5 (8.3%)
Educational High School 3 (9.1%) 4 (14.8%) 4 (14.3%) 3 (9.4%) 7 (11.7%)
Attainment Vocational 11 (33.3%) 8 (29.6%) 12 (42.9%) 7 (21.9%) 19 (31.7%)
Academic 18 (54.5%) 11 (40.7%) 9 (32.1%) 20 (62.5%) 29 (48.3%)
Israel 20 (60.6%) 13 (48.1%) 17 (60.7%) 16 (50%) 33 (55%)
Europe 3 (9.1%) 2 (7.4%) 2 (7.1%) 3 (9.4%) 5 (8.3%)
USA 1 (3%) 1 (3.7%) 1 (3.6%) 1 (3.1%) 2 (3.3%)
Asia 1 (3%) 5 (18.5%) 3 (10.7%) 3 (9.4%) 6 (10%)
Africa 3 (9.1%) 1 (3.7%) 1 (3.6%) 3 (9.4%) 4 (6.7%)
Soviet Union 5 (15.2%) 4 (14.8%) 4 (14.3%) 5 (15.6%) 9 (15%)
Place of Birth
Other 0 1 (3.7%) 0 1 (3.1%) 1 (1.7%)
T. Ricon et al. / Health 5 (2013) 179-187
The tool was found to distinguish between different
age groups (t = 5.46, p < 0.0001) [43] thus establishing
the construct validity of the Hebrew ACS and its appro-
priateness for a population of older Israelis with and
without disabilities. In another study conducted in Israel,
the known-groups method was used to support construct
validity between groups with and without a disability
[41]. This study also revealed a significant correlation
between the test and retest results (r = 0.897).
2.2. Study Procedure
The ethics committee of the University of Haifa
granted approval for this study. To increase inter-rater
reliability, the OT student examiners who administered
the study instruments (i.e., the first two authors) experi-
enced the assessment and scoring process themselves
before conducting assessments in the community Data
collection in the community took place in the summer
over a period of 4 months.
Participants were first approached through a prelimi-
nary telephone inquiry during which the examiners ad-
ministered the screening questionnaire orally. Subse-
quent data collection was conducted by the same exam-
iner in the participant’s home in comfortable conditions
(lit room with a comfortable chair and table). The pur-
pose of the project was explained to participants, who
were informed that their data will remain anonymous,
after which all of the participant gave signed consent to
participate in the study. Following this, all participants
completed the demographic questionnaire and MMSE
screening test.
The ACS assessment was then administrated in its
original version, which asks participants to sort the cards
into the categories of: “Never done before”, “Haven’t
done since I was 50/60” (depending on participant’s age),
“Doing now”, “Doing less”, and “Gave up”. Each acti-
vity domain was scored separately and a total score was
summed. The sum of the activities the participant was
“Doing now” plus those s/he was “Doing less” consti-
tuted the current activity (CA) score. The sum of previ-
ously performed activities, which included all the activi-
ties the participant had classified as “Doing now”, “Do-
ing less” and “Gave up”, were regarded as activities that
had been “Done in the past” and constituted the past ac-
tivity (PA) score. A retained activity score was calcu-
lated by dividing the number of CA by the number of PA,
with the result expressed as a percentage.
After the researcher had encoded the data obtained
from the original ACS version, the participant was in-
structed to “please go over the cards again and place
each picture of an activity you plan to do in the future
under the category of ‘I plan to do in the future’. This
includes activities that you participate in now and plan
on continuing, activities in which you participated in the
past and plan to return to, and activities that you never
participated in before but plan to start doing”. Scoring
after the addition of the new category was repeated for
the whole assessment and for each activity domain sepa-
rately, after which new scoring categories were created.
The future activity (FA) score was the total sum of future
activities in all the test domains. Within this total, the
new FA score was the total sum of activities the partici-
pant mentioned s/he had not participated in the past but
was planning to do in the future. The continuing FA
score was a total sum of activities a participant men-
tioned s/he had done in the past and was planning to do
in the future. A novelty percentage score was obtained by
dividing the new FA score by the continuing FA score.
The test-retest reliability of the expanded ACS was
measured on 20 of the original participants by re-admini-
stering the new version of ACS a second time, 7 - 8
weeks later.
2.3. Data Analysis
The data were analyzed using SPSS (Version 16). De-
scriptive statistics means, and standard deviations (SD)
were used to present the percentage of novelty and FA
scores. Pearson’s correlations examined the correlation
between: future planned activities (innovation and con-
tinuity) and the past activity (PA vs. FA) levels; and the
current and future activity (CA vs. FA) levels. Construct
validity using the known-groups method included com-
parisons between the two age groups (55 - 64, 65 - 74
years) and genders through the use of a two-way
MANOVA. Test-retest reliability was examined by us-
ing MANOVA repeated measures testing and Pearson
correlations. p-values of 0.05 or less (p 0.05) were con-
sidered significant.
The known-groups method is a common method of
supporting construct validity. The known-groups meth-
ods evaluates the test’s ability to discriminate between
the groups based on the groups demonstrating different
mean scores on the test.
3.1. Correlations between Past and Future
Activity Levels and between Current
and Future Activity Levels
Pearson’s correlations revealed a high significant cor-
relation between PA vs. FA levels (r = 0.71; p < 0.001)
and between CA vs. FA levels (r = 0.77; p < 0.001). Cor-
relations were found both for the total ACS total score
and for each domain score independently (Table 2).
These results indicate that participants who were more
active in the past and/or are more active in present plan
to continue being active in future.
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T. Ricon et al. / Health 5 (2013) 179-187 183
Tab le 2. Pearson correlations between activity card sort (ACS)
scores: future activities compared to past and current activities.
Correlation (r) with planned
future activity
ACS domain Past activities Current activities
Total score (for all domains) 0.71 0.77
IADL domain 0.51 0.59
Social-cultural domain 0.58 0.70
Low physical leisure domain 0.60 0.65
High physical leisure domain 0.60 0.74
Note: all values are significant at the p < 0.001 level.
3.2. Percentage of Novelty
A third of the participants (n = 20; 33.3%) planned on
continuing FAs only, that is, only FAs that were familiar
from current or past engagement in them. However, most
of the participants (n = 40; 66.7%) intended to participate
in a mixture comprising 16 - 48 continuing FAs (
= 49
activities) and a few (5) new FAs. Two-way MANOVA
revealed no significant differences between the age or
gender groups. No age-gender interaction effect was
found with respect to the novelty and total scores for any
test domain (data not shown).
3.3. Future Activity and Educational
Pearson’s correlations revealed a low positive signifi-
cant correlation (r = 0.39; p 0.05) between participants’
educational attainment and their FA score. Greater edu-
cational attainment correlated with an increased number
of planned FAs.
3.4. Future Activity among Age and Gender
Subgroups: Construct Validity
A two way MANOVA compared the mean FA scores
in the four ACS domains between the age and gender
groups. Significant differences were found between age
groups (F(4, 53) = 3.39; p = 0.02, η2 = 0.20) and gender
groups (F(4, 53) = 4.21; p = 0.001; η2 = 0.24) (see Table
1), but no interaction effect was found between them.
One-way ANOVA showed significant differences be-
tween the FA scores of the two age groups with respect
to the following test domains: social-cultural, low physi-
cal leisure, and high physical leisure. A difference be-
tween the genders was found only for the high physical
leisure domain (see Table 3).
Similar results were obtained when the educational at-
tainment variable was kept constant. Differences were
found between age groups but not between genders. No
interaction effect was found between age and gender on
the FA score.
3.5. Test-Retest Reliability of the Modified
A MANOVA repeated measures test showed no sig-
nificant difference between the tests (see Table 4).
The Pearson correlation test values obtained between
the test and retest scores (Table 5) indicate that the test-
retest reliability for most of variables is moderate-high.
Hence, the number of activities planned for the future
was similar in both test administrations.
The current study tested an expanded version of the
ACS assessment that allows occupational therapists to
obtain information on an individual’s future activity
plans in addition to examining past and present participa-
tion in leisure activities. The main question of the current
study was, do older adults tend to continue former or
current daily and leisure activities in the future or do they
engage in new ones.
4.1. Continuity in Old Age
The results of the current study support the idea that
older adults seek continuity between past and current
activities when planning their future [10,12,13]. The
continuity motive indicates that older adults desire sta-
bility with respect to behavior and activity patterns
throughout their lives. They achieve continuity by main-
taining existing or previous activity patterns into old age.
This implies that a person who was active in his younger
years will seek opportunities to stay active in the future
[13]. The same tendency is manifested in this study,
which indicates that the more active people are or were
in the past, the more future activities they plan to par-
ticipate in. The stability of leisure behavior throughout
the lifespan can be viewed as a strategy assisting people
to cope with the changes associated with ageing [19] and
provides the ageing person with a way of maintaining
internal continuity through external continuity [11]. The
high correlations between the past, present, and future
activity scores for the overall test, as well as for each
domain, also indicate that patterns of activity remain
stable throughout life and so lend further support to the
continuity motive. People tend to maintain those activi-
ties they have engaged in present or past and not to plan
new ones.
4.2. Innovation in Old Age
Innovation appeared to a similar extent in both age and
gender groups. Some of the participants wished to en-
gage in new activity experiences in the future (such as
volunteering activities or activities with grandchildren).
It can be assumed that the choice of activity reflects
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T. Ricon et al. / Health 5 (2013) 179-187
Copyright © 2013 SciRes. OPEN A CCES S
Table 3. Age and gender group means ± SD and F values of ACS future activity scores.
F (η2) (Observed power) Gender Age group
Gender Age
(n = 28)
(n = 32)
Elderly adult
group (n = 27)
Older adult
Group (n = 33)
0.65 12.01* (0.18) (0.93) 50.28 ± 11.8350.63 ± 12.6745.26 ± 14.01 55.18 ± 8.27 Total score
0.76 2.78 14.32 ± 3.53 13.78 ± 4.29 13.15 ± 4.01 14.76 ± 3.78 IADL score
0.002 5.65* (0.09) (0.65) 14.96 ± 3.51 15.41 ± 3.83 13.96 ± 4.12 16.21 ± 2.92 Social-cultural
0.11 5.42* (0.09) (0.63) 12.39 ± 3.69 13.34 ± 5.13 11.37 ± 4.92 14.15 ± 3.76 Low physical leisure
7.66** (0.12) (0.78)11 . 8 2** (0.17) (0.62) 9.04 ± 3.63 7.19 ± 3.60 6.59 ± 3.62 9.24 ± 3.37 High physical leisure
*p < 0.05; **p < 0.01.
Table 4. Repeated measure MANOVA scores-test and retest means ± SD and F values for past, current, and future activities.
F Re-test Test Variablea
0.08 16.82 (±2.60) 17.00 (±1.61) IADL past
0.82 17.27 (±2.45) 18.09 (±3.11) SocCult past
0.02 16.55 (±4.32) 16.64(±5.33) PhysLow past
0.23 10.91 (±3.33) 11.36 (±2.87) PhysHigh past
0.12 14.68 (±2.83) 14.86 (±1.80) IADL present
10.23 12.86 (±2.60) 13.55(±2.45) SocCult present
0.03 12.14 (±3.91) 12.00 (±3.58) PhysLow present
0.06 6.95 (±2.62) 6.77 (±2.41) PhysHigh present
0.12 15.55 (±2.73) 15.36 (±1.80) IADL future
0.00 15.91 (±3.05) 15.91 (±2.84) SocCult future
40.22 15.09 (±5.38) 13.18 (±4.00) PhysLow future
0.62 8.64 (±3.11) 7.91 (±2.70) PhysHigh future
aThe four domains of the Activity Card Sort (ACS) served as the study variables: IADL, instrumental activities of daily living; SocCult, social-cultural activities;
PhysLow, low-physical leisure activities; PhysHigh, high-physical leisure activities.
Table 5. Pearson correlations tests between test and retest scores of the ACS total score and sub test scores in past, present and future.
p r (p) Post-Test Pre-Test Va riable
0.000*** 0.81 63.8 (±9.06) 64.7 (±8.35) Total Past
0.003** 0.64 17.45 (±2.61) 17.65 (±1.76) IADL Past
0.072 (ns) 0.41 17.85 (±2.21) 18.35 (±2.5) HevTar Past
0.000*** 0.88 16.95 (±3.79) 17.3 (±4.55) PhysLow Past
0.001** 0.66 11.25 (±3.27) 11.45 (±2.76) PhysHigh Past
0.000*** 0.72 46.8 (±7.27) 47.83 (±7.4) Total Present
0.000*** 0.79 14.98 (±2.28) 15.5 (±1.93) IADL Present
0.002** 0.66 13.48 (±2.2) 14.1 (±2.04) HevTar Present
0.000*** 0.72 11.93 (±3.06) 12.3 (±3.14) PhysLow Present
0.028* 0.49 6.43 (±2.32) 6.75 (±2.58) PhysHigh Present
0.000* 0.79 53.7 (±11.01) 52.4 (±9.82) Total Future
0.011* 0.56 15.2 (±3.05) 15.36 (±2.01) IADL Future
0.000*** 0.84 15.95 (±2.95) 15.5 (±2.78) HevTar Future
0.000*** 0.75 3.8 (±4.43) 13.2 (±3.46) PhysLow Future
0.000*** 0.75 8.75 (±4.02) 8.1 (±3.49) PhysHigh Future
T. Ricon et al. / Health 5 (2013) 179-187 185
changed life circumstances. For example, a retired per-
son might seek ways of filling free time with a new ac-
tivity, and a person who had experienced the birth of a
first grandchild might choose to spend time with the
The number of new FAs was rather low compared to
the number of continuing FAs, indicating that the main
tendency is to continue previous activities. We further
observe that innovation is related to continuity, in that
even some of the new activities chosen were an expan-
sion of previous activities. For example, a participant
who had engaged in certain craft activities in the past
planned different craft activities for the future.
Previous studies showed that the phenomenon of in-
novation in old age is yet to be explored. The question
whether innovation in old age is common or not is not
definite, with some evidence showing it is not common
[20-22], and some showing it is [23].
This study’s findings of low levels of innovation in
both gender and age groups are consistent with those of
Iso-Ahola and colleagues [20], Levinson [21] and Parker
[22] and support the expected construct validity. Nimrod
[23] found that women exhibit more innovation than men,
whereas this study found no significant difference be-
tween the two genders.
4.3. Test-Retest Validity
Results remained stable when participants were re-
tested several weeks later. Thus, the new version of the
ACS has moderate-strong test-retest validity and we an-
ticipate stable results across repeated testing.
Significant differences were found between the two
age groups in three activity domains (low physical, high
physical and social-cultural leisure), with the older age
group (elderly adults) participating less than the younger
age group (older adults). These results support previous
findings that there is a correlation between age and lei-
sure participation [47]. In the IADL domain there was no
difference between the age groups. A possible explana-
tion might be that IADL are complex daily activities
(such as preparing meals or doing household chores).
These activities are vital to daily functioning at all ages
[36]; hence healthy older people plan to maintain doing
them throughout their ageing [48].
There were significant differences between men and
women only in the high physical leisure domain. It
seems that men and women have different needs and
experiences of leisure, which lead them to choose dif-
ferent leisure activities. Previous studies have found that
women prefer home-based social activities and spending
time with family, while men prefer active social activi-
ties involving less direct communication with others and
highly physically demanding leisure activities [44,49,50].
The findings of the current study support the claim that
men prefer more highly physical leisure activities than
The study was based on a convenience sample, and is
therefore unrepresentative of the general population. It is
recommended to conduct additional research on a larger,
more representative sample.
Participants were not presented with a definition of
“future” prior to their undertaking the “future planning”
sorting task. As a result, the future could be understood
as extending indefinitely from the next moment. We
suggest providing participants with a definition of “fu-
ture” in subsequent research using this tool.
In order to intervene with older clients, occupational
therapists and other health professionals need to study
the tendencies of this population regarding the continuity
or innovation of future planned activities. The current
research aimed to develop such a tool by expanding the
ACS: an existing tool known to be suitable and valid for
use with this population and which already enjoys wide-
spread popularity. We found our expanded ACS to have
significant moderate-high known group validity and
test-retest reliability.
Use of the expanded test will enable practitioners to
gain knowledge regarding the future activities planned
by clients, and assist in establishing treatment goals in a
client-centered intervention. This study supports existing
knowledge of the process of future planning and adds to
the scant literature regarding the older population and
their future planning of IADL and leisure activities.
The new version of the ACS was found to be suitable
in evaluating the past, present, and future activity profile
of participants and, following additional testing, is ex-
pected to become a robust tool for conducting precise
and reliable evaluations of the elderly population.
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