A new category of “future planning” in the activity card sort: Continuity versus novelty in old age

doi:10.4236/health.2013.52025

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Vol.5, No.2, 179-187 (2013) Health

http://dx.doi.org/10.4236/health.2013.52025

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: [email protected]

Received 30 November 2012; revised 29 December 2012; accepted 6 January 2013

ABSTRACT

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

1. INTRODUCTION

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-

T. Ricon et al. / Health 5 (2013) 179-187

180

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

occupations.

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.

2. METHOD

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

T. Ricon et al. / Health 5 (2013) 179-187

181

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

study.

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.

Variable

Older

(55 - 64 years)

(n = 33) (55%)

Elderly

(65 - 74 years)

(n = 27) (45%)

Male

(n = 28)

(46.7%)

Female

(n = 32)

(53.3%)

Total

(n = 60)

(100%)

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

182

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. RESULTS

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.

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

Attainment

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

ACS

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.

4. DISCUSSION

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

T. Ricon et al. / Health 5 (2013) 179-187

184

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

Male

(n = 28)

Female

(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

newborn.

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

women.

5. LIMITATION

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.

6. CONCLUSIONS

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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