HEALTH EDUCATION RESEARCH
Theory & Practice
Vol.18 no.3 2003
Pages 304–317
Acceptability and feasibility of a computer-tailored
physical activity intervention using stages of change:
project FAITH
Corneel Vandelanotte and Ilse De Bourdeaudhuij
Abstract
This study investigated the feasibility and
acceptability of a new computer-tailored intervention promoting physical activity in a general
population, and explored if there are differences
in the reported feasibility and acceptability
between stages of change, gender, age groups,
education levels and familiarity with computer
use. The computer-tailored intervention program consists of questionnaires concerning
demographics, physical activity and psychosocial determinants, leading to a ‘physical activity
advice’ and an ‘action plan’. This feedback was
constructed taking the stages of change into
account, at content level as well as in the way
participants were approached. One hundred
and ninety-two participants, between 25 and 55
years of age, ran through the tailored materials,
and completed an acceptability and feasibility
questionnaire afterwards. This questionnaire
contained feasibility and acceptability questions
about all the intervention aspects: intervention
questions, physical activity advice, action plan
and computer use. High acceptability and feasibility scores were found for all intervention
parts. Only a few significant differences in
acceptability and feasibility scores between
stages of change, gender, age groups, education
levels and familiarity with computer use were
found. These results suggest that this computerDepartment of Movement and Sport Sciences, Faculty of
Medicine and Health Sciences, Ghent University,
Watersportlaan 2, 9000 Ghent, Belgium.
E-mail: [email protected]
© Oxford University Press 2003. All rights reserved
tailored intervention is an acceptable and feasible tool for promoting physical activity for
respondents having different stages of change,
ages, gender, education levels and computer use.
Introduction
Epidemiological and controlled clinical studies
have consistently reported the positive health benefits of physical activity (Paffenbarger et al., 1986;
Morris et al., 1990; Dishman, 1992) and, conversely, the negative effects of physical inactivity
(McGinnis and Foege, 1993). Regular participation
in physical activity reduces the risk of premature
mortality, coronary heart disease, hypertension,
colon cancer and diabetes mellitus (Surgeon
General, 1996; Vuori, 1998). Physical activity also
appears to reduce depression and anxiety, improve
mood, and enhance ability to perform daily tasks
throughout the life span (Surgeon General, 1996).
In order to receive these health benefits, adults
should accumulate at least 30 min of moderateintensity physical activity on most, preferably all,
days of the week (Council of Europe, 1995; NIH
Consensus Statement, 1995; Pate et al., 1995).
Intermittent or shorter bouts of activity (at least
10 min) also have similar cardiovascular and health
benefits if performed at a level of moderate intensity and if accumulated to at least 30 min a
day (Council of Europe, 1995; NIH Consensus
Statement, 1995). Despite the well-documented
health benefits and public health efforts to increase
physical activity, most adults remain under-active
and only a limited share of the population comply
with these recommendations. World-wide, only
15–25% of the adult population engage in vigorous
doi: 10.1093/her/cyf027
Acceptability and feasibility of a computer-tailored physical activity intervention
physical activity, about 35–50% engage in some
physical activity of moderate intensity and 30–
45% are completely inactive (Caspersen et al.,
1994; Bouchard et al., 1994; Crespo et al., 1996;
Phillips et al., 1996; Surgeon General, 1996; Pratt
et al., 1999; Sallis and Owen, 1999). Similar results
have been found for Europe and, more specifically,
for Belgium (De Backer et al., 1981; De
Bourdeaudhuij and Van Oost, 1994; Aelvoet et al.,
1997; Steptoe et al., 1997; Kearney et al., 1999;
Margetts et al., 1999; Vaz et al., 1999; Rzewnicki
et al., 2001).
Interventions aimed at increasing physical activity levels in a variety of populations have met
with limited success. Some studies have shown
improvements, but changes are often short lived
and programmes frequently suffer high rates of
drop out (Buxton et al., 1996; Dishman and
Buckworth, 1997). Furthermore, the most effective
interventions were also very time consuming and
expensive, often impractical, and reached only a
small part of the general population (Annesi, 1998).
However, technological developments in the
field of computer science and telecommunications
made a new kind of intervention possible, i.e.
computer-tailored interventions (Kreuter et al.,
2000). Computer-tailored interventions provide
respondents with personally adapted feedback
about their present health behavior and/or the
behavioral determinants. They also provide personally adapted suggestions to change behaviors that
are potentially health threatening and to maintain
behaviors that are beneficial for health (De Vries
and Brug, 1999; Brug and van Assema, 2000). The
specific personal information needed to produce
personally adapted feedback is obtained through
questionnaires targeting a specific health behavior,
such as smoking, diet, physical activity, cancer or
cholesterol screening. One-to-one counseling is
thus mimicked by using an expert interactive
computer, which is often able to produce feedback
immediately. Although computerized tailoring is
not a traditional mass media approach, it can reach
many individuals in a relatively cheap way, provide
personal information, give less redundant information and ensure confidentiality for the user
(De Vries and Brug, 1999). A further advantage
of tailored interventions is that they allow the
stages of behavioral change to be considered.
In the Transtheoretical Model (Prochaska et al.,
1992), the process of behavior change is described
as a multi-stage process. Five stages are distinguished in this model. People move from precontemplation (no intention to change within 6 months),
to contemplation (intention to change within 6
months), to preparation (intention to change within
30 days), to action (actually changing) and, finally,
to maintenance of the behavior changes. The model
describes a dynamic process whereby individuals
at different stages use different processes to consider and adopt new behaviors. Interventions specifically targeted at an individual’s stage of change
are more effective in promoting that change
(Prochaska et al., 1993; Oldenburg et al., 1999;
Peterson and Aldana, 1999).
In a growing number of well-designed studies
addressing a range of health-related behaviors and
other outcomes, tailored health communication
materials outperform non-tailored materials
(Skinner et al., 1999; Kreuter et al., 2000). However, little is known about the specific mechanisms
that drive the effectiveness of tailoring and some
authors (Skinner et al., 1999) call for studies to
explore what is in the ‘black box’ of tailored
interventions. A possible explanation is presented
by the elaboration likelihood model (Petty and
Cacioppo, 1981), which suggests that people are
more likely to thoughtfully process information
when they perceive it to be personally relevant.
Thus, tailored materials which address an individual’s specific problems and concerns should be
more likely to stimulate change than untailored
materials. This theory has been supported in a
study by Kreuter et al. (Kreuter et al., 1999a) and
several studies have reported findings consistent
with these expectations (Brug et al., 1996; Brug
and van Assema, 2000). Compared with nontailored messages, tailored messages are more
likely to be read and remembered, saved and
discussed with others, perceived as interesting,
personally relevant, and written especially for them
(Brug et al., 1996; Skinner et al., 1999). However,
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C. Vandelanotte and I. De Bourdeaudhuij
previous studies have also been open to other
explanations of their effect. It could be argued
that tailored interventions often simply provide
participants with more information or it might just
be the ‘active participation’ (participants filling in
questions about the health behavior) that is causing
the effect (Contento et al., 1995).
A drawback of computer-tailored interventions
is that the feedback received by participants
depends on their own self-report measurements,
which may not always be accurate and might cause
wrong estimates of the examined health behavior.
Although the population reached with computertailored interventions can be very large, selection
is almost inevitable since not everyone has access
to a computer or is used to handling one. Several
authors also state that tailored interventions, compared to untailored interventions, are useless in a
population with almost no variation, since there is
little need to tailor a message if it would end up
being more or less the same for all members of
the intended audience (Bull et al., 1999b; Kreuter
et al., 1999b). Further, it seems that although
tailored materials are better read and remembered
than non-tailored materials (Skinner et al., 1999),
a large proportion of participants still do not read
or remember the tailored materials at all (Skinner
et al., 1994; Kreuter and Strecher, 1996). In addition, despite the goal of tailoring to produce highly
individualized communication, only about half of
participants say the materials they received ‘apply
to me specifically’ (Brug et al., 1996, 1998). This
shows that tailored interventions can have much
potential, but careful research is still necessary to
prove their additional value.
Within the field of physical activity promotion,
some authors showed that tailored interventions
were effective. In a study by Marcus et al. (Marcus
et al., 1998a), tailored and non-tailored physical
activity promotion materials were compared. The
experimental group received tailored feedback. The
non-tailored condition received standard self-help
information. All participants received mailings at
baseline, 1, 3 and 6 months. The experimental
group outperformed the self-help group on all
outcome measures: minutes of physical activity
306
per week, meeting international physical activity
recommendations and achieving the action stage
of motivational readiness for physical activity
adoption. In the follow-up study (Bock et al.,
2001), 12 months later, the experimental group
still surpassed the self-help group. Another study
by Marcus et al. (Marcus et al., 1998b) compared
the efficacy of an intervention tailored to the
individual’s stage of motivational readiness for
exercise adoption with a standard exercise promotion intervention. Results showed that, compared
to the standard intervention, those receiving the
tailored intervention were significantly more likely
to show increases (37 versus 27%) and less likely
to show either no change (52 versus 58%) or
regression (11 versus 15%) in the stage of motivational readiness. Another research team (Bull et al.,
1999b) conducted a randomized controlled trial
among 272 adults to compare effectiveness of
tailored, personalized (names of participants mentioned), general and no health messages promoting
physical activity. Participants in the tailored group
were more likely to increase physical activities of
daily living than were participants in personalized,
general and control groups (65 versus 46 versus
56 versus 54%), and less likely to report ‘doing
less’ at follow-up (18 versus 38 versus 38 versus
38%).
Other tailoring studies showed positive effects
on physical activity (Kreuter and Strecher, 1996),
but not all of them did so. In a study by Bull et al.
(Bull et al., 1999a), participants received either
tailored or standard information on physical activity after visiting a physician. Follow-up at 12
months did not reveal any significant differences
between groups for physical activity and movement
across stages of readiness to exercise.
These findings indicate that tailoring might be
a promising way of intervening in physical activity
promotion, yet only a few studies examined the
effectiveness of these physical activity interventions, with varied success. This emphasizes the
need for more information to determine the effectiveness and appropriateness of using tailored interventions to promote physical activity. However,
this new trend for developing computer-tailored
Acceptability and feasibility of a computer-tailored physical activity intervention
interventions all over the world also includes the
risk of thoughtless implementation. The construction of a computer-tailored program implies the
development of hundreds of messages, which are
written to be specifically relevant for every single
participant. In this respect, feedback messages
create the illusion of personal interaction and
tailoring. In contrast, all interaction between participants and the intervention providers takes place
through computers. There is no one to assist
participants or to make adjustments when something goes wrong or when incorrect feedback
appears. This argues for extensive acceptability
and feasibility testing of a computer-tailored intervention before implementation. Moreover, Tones
and Tilford (Tones and Tilford, 2001) underline
that acceptability and feasibility testing (also called
pre-testing) must form part in the development of
any well-designed health education programme,
and that it is also an integral part of the process
of evaluation. As Kreuter et al. (Kreuter et al.,
2000) argue, acceptability and feasibility testing
of computer-tailored messages is different and
more complex than acceptability and feasibility
testing of non-tailored materials. Each participant
has to receive the actual messages he or she would
get if participating in the health program. If tailored
messages are evaluated by a person for who the
message was not meant, they will be rated as
unsuitable an irrelevant (Kreuter et al., 2000). It
is clear that acceptability and feasibility testing
does not guarantees that a program will be effective, but it increases the likelihood that the intervention is comprehensive, relevant, noticeable,
memorable, credible, acceptable and attractive,
which are prerequisites for attitude and behavior
change (Weinreich, 1999).
The main aim of this study is to investigate
acceptability and feasibility of a recently developed
computer-tailored intervention promoting physical
activity in participants in different stages of change.
We wish to test usability, user-friendliness, credibility, feasibility, comprehensibility, readability and
related factors in a general population. We especially wish to explore whether there are differences
in the reported feasibility and acceptability of
Table I. Descriptive characteristics (percentages or means ⫾
SD) for total sample
Total sample
(n ⫽ 192)
Sex (%)
male
female
Age
⬍40 years
⬎40 years
Working situation (%):
working
career interruption
housewife/husband
other (e.g. student)
Job category (%)
white collar
executive position
self-employed
laborer
Education (%)
high (university or college)
low (secondary school or lower)
Body mass index (mean ⫾ SD)
Residential area (%)
city
suburb
village
Stage of changes (%)
precontemplators
contemplators and preparators
action and maintenance
52.1
47.8
59.4
40.6
88.0
0.5
2.1
9.4
76.2
10.5
4.7
4.7
75
25
23.69 ⫾ 3.04
19.8
35.9
44.3
40.3
39.8
19.8
the computer intervention between individuals of
different stages of change, gender, age groups,
education levels and familiarity with computer use.
Methods
Participants and procedure
Participants were recruited at random in and around
the city of Ghent (Belgium). They were between
25 and 55 years of age. A total of 192 adults
participated in this study. Table I presents an
overview of the descriptive characteristics of the
participant.
Each participant received a computer disk containing the tailored physical activity intervention,
and an acceptability and feasibility questionnaire
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C. Vandelanotte and I. De Bourdeaudhuij
to evaluate the intervention. All instructions and
information participants needed to know were
printed on the questionnaire itself. It said they
were participating in an acceptability and feasibility
testing of a newly developed physical activity
intervention, and that they had to be as critical as
possible about the intervention. When recruiting,
participants were also given that information. Participants independently ran through the intervention
wherever they had a computer at their disposal
(work, home, family), filling in parts of the selfadministered acceptability and feasibility questionnaire at the same time. Altogether, this took about
50–60 min. After completion, the research team
personally collected the questionnaires and the
intervention materials. Participants without a computer could use a portable computer temporarily
provided by the research team.
Intervention
The intervention (also called ‘Tailored Movement
Advice’) is part of the FAITH project (Fat and
Activity Intervention Tailored to Health) and aims
to increase physical activity in those participants
who do not meet the current recommendations on
physical activity (Pate et al., 1995) by means of
specific individualized feedback. This intervention
belongs to the second generation of tailoring interventions (Brug and van Assema, 2000), in which
interactive computer programs are used to provide
immediate feedback on the computer screen. An
introduction page leads participants to one of two
main parts, i.e. the ‘physical activity advice’ or
the ‘action plan’.
In order to receive the physical activity advice
(or feedback), participants first have to fill in three
questionnaires. The first one measures participants’
demographic factors. The second one measures
physical activity using the International Physical
Activity Questionnaire (IPAQ). This questionnaire
was developed by a working group, initiated by
the WHO, and the Centers for Disease Control
and Prevention, attending a Physical Activity
Standardization Meeting in Geneva, Switzerland,
in April 1998. The purpose of the questionnaires
is to provide a common instrument that can be
308
used internationally to obtain physical activity
surveillance system data. Validity and reliability,
results presented at the ACSM 2000 conference
(Pratt et al., 2000), are acceptable. The third one
measures psychosocial determinants of physical
activity (knowledge, social support, self-efficacy,
attitudes, perceived benefits and barriers, intentions, and environment) and was based on previous
research (Deforche and De Bourdeaudhuij, 2000;
De Bourdeaudhuij and Sallis, 2002; De
Bourdeaudhuij et al., 2003).
From the moment participants have answered
all the questions the computer feedback appears.
Feedback is selected out of a database filled with
messages that match any possible combination of
answers. Feedback is immediately displayed on
screen, and is based on the Theory of Planned
Behavior (Ajzen, 1985) and the Transtheoretical
Model (Prochaska et al., 1992). The Theory of
Planned Behavior was considered by giving feedback about participants’ intentions, attitudes, selfefficacy, social support, knowledge, benefits and
barriers of physical activity. The stages of changes
were considered in two ways. First, the content
differed between stages. Precontemplators mainly
received general information about an active lifestyle and information about its benefits. To avoid
resistance, the need for behavior change was not
dictated, but only indirectly suggested. Contemplators received the same information, although not
so extensively, and it was mentioned that they
might benefit from becoming active themselves.
In the preparation stage, the emphasis really was
on becoming more active, combined with specific
physical activity and health information. In the
action stage, the emphasis was on staying active
and relapse prevention. In the maintenance stage,
feedback was reduced to saying that they were
doing well and that they should carry on. Second,
the way in which the participants were approached
also differed between stages. Information for precontemplators was presented in an impersonal way
(e.g. people could...), again avoiding resistance.
Contemplators were approached in a personal way
(e.g. you could...), but not in a decisive way which
was used for preparators (e.g. you should...) or a
Acceptability and feasibility of a computer-tailored physical activity intervention
supporting way used for people in the action or
maintenance phase (e.g. you do...). In practice, this
means that, after a general introduction, normative
feedback is presented by relating participants’
physical activity to current recommendations, followed by tips on how to increase (if needed)
physical activity at work, household chores, gardening, leisure-time and transportation. Information about what a sports partner is, how to join a
sports club, what advantages originate from physical activity, how to deal with barriers associated
with physical activity and how to overcome a low
self-efficacy is also included. An active lifestyle
as well as taking up a specific sports activity is
promoted. Altogether feedback can amount to as
much as five or six pages of advice.
The action plan operates independently of the
physical activity advice, and is meant only for
those participants who are not in the maintenance
stage and are also motivated to become more
active. It consists of two questionnaires. The first
one measures participants’ demographic factors.
The second one is used to transform physical
activity intentions into specific acts, and therefore
asks people what activity they want to do when,
where, how long and with whom. The questions
aim to start a process of thought which directs
people how to become more active (implementation intentions). The action plan itself is an exact
reproduction of the answers participants gave and
can be used to remind them of their physical
activity intentions. The action plan in this study
can be compared to the ‘physical activity plan’ in
the PACE study (Physician-based Assessment and
Counseling for Exercise), in which almost the
same questions have to be answered (Patrick et al.,
1994; Calfas et al., 1997; Prochaska et al., 2000).
Acceptability and feasibility questionnaire
There is no theory or model about how to measure
acceptability or feasibility of tailored interventions.
Nor are there guidelines about what acceptability
or feasibility scores are ‘good’ or ‘bad’, or what
level of acceptability is needed to predict behavior
change in tailored interventions. However, in the
literature, several concepts are raised which are
assumed to be important in pre-testing intervention
materials. These concepts can be summarized as
intervention usability, user-friendliness, credibility,
feasibility, comprehensibility and readability (Brug
et al., 1996; Weinreich, 1999; Kreuter et al., 2000;
Prochaska et al., 2000; Tones and Tilford, 2001).
For the present study, a self-administered questionnaire was developed including these concepts,
and based on existing questionnaires assessing
feasibility and acceptability of Dutch nutrition
interventions (Brug et al., 2000; Maes et al., 2000).
The use of a self-administered questionnaire has
been indicated as a standard pre-testing technique
(Tones and Tilford, 2000).
The questionnaire consists of three parts. Part 1
contains questions about participants’ demographics (gender, age, weight, height, work situation, job category, education and residential area)
and stages of change. Based on three questions,
participants were classified into a stage of change
(Curry et al., 1992; Greene and Rossi, 1998). First
question: ‘Are you planning to do more physical
activity than you do now (no/yes, within 6 months/
yes, within 1 month)?’. Second question: ‘In the
past 6 months, did you do more or less physical
activity (more/as much/less)?’. Third question:
‘How many days a week are you, at least 30 min,
physically active at moderate intensity or higher?’.
These questions allow participants to be grouped
into five stages of change. In Part 2 of the questionnaire, participants could write down suggestions
and remarks concerning the computerized intervention. Part 3 consists of questions (five-point Likert
scales: 1 ⫽ ‘I don’t agree at all’ to 5 ⫽ ‘I totally
agree’) about intervention questions: ‘I think the
intervention questions are comprehensible, logical,
in good order, easily readable,...’ (nine items);
about the physical activity advice: ‘I think the
physical activity advice is interesting, credible,
logical, comprehensible, personal relevant, confusing, complete, too long,...’ (12 items); concerning
the action plan: ‘I think the action plan is relevant,
credible, complete, well-styled, logical,...’ (10
items); and, finally, about using a computer for
this intervention: ‘I think the computer program
is users friendly, clear, a good choice for this
309
C. Vandelanotte and I. De Bourdeaudhuij
intervention, well-styled,...’ (nine items). This
resulted altogether in 39 questions. Before the
acceptability and feasibility questionnaire was
used, it was first tested among 15 people not
participating in this study.
Statistical analyses
All the analyses were performed using SPSS 10.0.
The 39 items on the acceptability and feasibility
questionnaire were reduced to seven scales and
eight single items. Items that matched well were
put together in a scale, making it easier to overview
and interpret the results. Cronbach’s αs were used
to control for internal consistency between these
items. However, some items were too important
to be merged and in order to allow unequivocal
interpretation they were kept single. In the results
section, item means (not scale means) are often
accompanied by percentages, providing additional
information. These percentages represent participants who answered ‘4’ (agree) or ‘5’ (totally agree)
on the acceptability and feasibility questionnaire
items. Independent-samples t-tests were used to
explore differences in gender (males and females),
age (25–40 and 41–55 years), education (low and
high) and computer use (used to and not used to)
on acceptability and feasibility items or scales.
One-way ANOVAs were used to explore differences in stages of change on acceptability and
feasibility items or scales. In order to have sufficient analysis power, contemplation and preparation, on the one hand, and action and maintenance,
on the other, were taken together so that three
motivational levels could be distinguished: (1)
precontemplators, (2) contemplators and preparators, and (3) respondents in the action and maintenance stage. Tukey test was used for post hoc
analyses. P ⬍ 0.05 was considered to be significant
for all analyses.
Results
Total sample means
In Table II, the total sample mean (4.04 ⫾ 0.56)
indicates that most participants reported that the
intervention questions are logical, easily readable,
310
in good order, easily to fill in and well-styled.
The same goes for the comprehensibility of the
questions (4.07 ⫾ 0.80). However, a considerable
number of participants also reported that there are
too many intervention questions (2.92 ⫾ 1.23).
When the percentage is calculated, it shows that
34% of them did indeed report that there were too
many questions.
The total sample mean (3.67 ⫾ 0.55) illustrates
that most participants reported that the physical
activity advice was interesting, logical, instructive,
comprehensible, well-styled and complete. In general, participants did not indicate that the advice
was too long, confusing or that it gives too much
information (2.43 ⫾ 0.82). Further, the credibility
of the physical activity advice was good (3.74 ⫾
0.84); more precisely; 68% of them were positive
about it. Over half (53.8%) reported that the advice
was relevant or useful (3.49 ⫾ 0.86). However, a
smaller mean score was found for the number of
participants reporting that they intended to use the
physical activity advice (3.23 ⫾ 1.04). This positive
intention was reported by about 43% of the participants.
Similarly, most participants reported that the
action plan was logical, well-styled, comprehensible, correct and complete (3.70 ⫾ 0.57). Further,
good credibility was also found (3.53 ⫾ 0.86).
However, a lower mean score suggests that only a
moderate number of participants indicated that the
action plan was useful, had added value on top of
the physical activity advice or would help them to
become more active (3.17 ⫾ 0.89).
The high total sample mean (4.09 ⫾ 0.69)
shows that a large number of participants indicated
that the computer program is user friendly, clear
and a good choice for this intervention. Only a
small proportion reported having problems with
colors, presentation and styling used in the program
(1.90 ⫾ 0.69). A large number of participants
reported being familiar with a computer (4.25
⫾ 0.99). Consequently only 10% of participants
reported that they did a pencil-and-paper intervention rather than a computerized intervention
(1.91. ⫾ 1.04)
3.67
(⫾ 0.55)
2.43
(⫾ 0.82)
3.23
(⫾ 1.04)
0.71
0.78
I think the physical activity advice is
interesting, logical, instructive,
comprehensible, well-styled and
complete (6 items)
I think the physical activity advice is
too long, confusing and gives too
much information (3 items)
I am going to use the physical activity
advice (1 item)
4.09
(⫾ 0.69)
1.90
(⫾ 0.83)
0.70
0.83
I think the computer program is userfriendly, clear and is a good choice for
this intervention (3 items)
I have problems with colors,
presentation and styling used in the
computer program (4 items)
1.96
(⫾ 0.84)
4.15
(⫾ 0.67)
1.84
(⫾ 1.05)
4.45***
(⫾ 0.91)
3.51
(⫾ 0.81)
3.72
(⫾ 0.55)
3.07
(⫾ 0.88)
3.65
(⫾ 0.55)
3.55
(⫾ 0.81)
1.84
(⫾ 0.83)
4.02
(⫾ 0.70)
1.98
(⫾ 1.03)
4.02***
(⫾ 1.03)
3.55
(⫾ 0.90)
3.68
(⫾ 0.57)
3.25
(⫾ 0.90)
3.15
(⫾ 1.06)
2.39
(⫾ 0.76)
3.68
(⫾ 0.56)
3.42
(⫾ 0.91)
3.75
(⫾ 0.91)
4.14
(⫾ 0.77)
2.88
(⫾ 1.23)
4.07
(⫾ 0.56)
1.85
(⫾ 0.79)
4.14
(⫾ 0.60)
1.83
(⫾ 1.00)
4.48****
(⫾ 0.85)
3.45
(⫾ 0.87)
3.61**
(⫾ 0.60)
3.06
(⫾ 0.89)
3.16
(⫾ 1.10)
2.40
(⫾ 0.83)
3.63
(⫾ 0.58)
3.44
(⫾ 0.83)
3.66
(⫾ 0.88)
4.10
(⫾ 0.80)
2.99
(⫾ 1.26)
4.02
(⫾ 0.52)
of change: 1⫽ precontemplators; 2 ⫽ contemplators and preparators; 3 ⫽ action and maintenance.
Significant difference between categories of a variable: *P ⬍ 0.1; **P ⬍ 0.05; ***P ⬍ 0.01; ****P ⬍ 0.001.
1.91
(⫾ 1.04)
I would rather do a written test
(1 item)
aStages
4.25
(⫾ 0.99)
Computer
I am familiar with using a computer
(1 item)
3.70
(⫾ 0.57)
3.53
(⫾ 0.86)
0.82
I think the action plan is credible
(1 item)
I think the action plan is logical, wellstyled, comprehensible, correct and
complete (6 items)
3.17
(⫾ 0.89)
3.31
(⫾ 1.02)
3.49
(⫾ 0.86)
I think the physical activity advice is
relevant or useful (1 item)
0.78
2.46
(⫾ 0.88)
3.74
(⫾ 0.84)
Physical activity advice
I think the physical activity advice is
credible (1 item)
Action plan
I think the action plan is useful, is a
added value on top of the physical
activity advice and will help me to
become more active (3 items)
4.00
(⫾ 0.82)
2.98
(⫾ 1.24)
4.07
(⫾ 0.80)
2.92
(⫾ 1.23)
3.73
(⫾ 0.76)
4.02
(⫾ 0.56)
1.96
(⫾ 0.88)
4.01
(⫾ 0.80)
2.04
(⫾ 1.09)
3.91****
(⫾ 1.10)
3.63
(⫾ 0.84)
3.82**
(⫾ 0.51)
3.31
(⫾ 0.89)
3.32
(⫾ 1.04)
2.47
(⫾ 0.81)
3.72
(⫾ 0.51)
3.55
(⫾ 0.91)
3.86
(⫾ 0.76)
4.01
(⫾ 0.80)
2.82
(⫾ 1.19)
4.07
(⫾ 0.61)
1.93
(⫾ 0.82)
3.94*
(⫾ 0.77)
2.06
(⫾ 1.04)
3.89***
(⫾ 1.22)
3.70
(⫾ 0.80)
3.72
(⫾ 0.44)
3.38*
(⫾ 0.68)
3.45*
(⫾ 1.02)
2.28
(⫾ 0.72)
3.72
(⫾ 0.57)
3.51
(⫾ 0.83)
3.80
(⫾ 0.86)
4.04
(⫾ 0.85)
2.71
(⫾ 1.22)
4.06
(⫾ 0.65)
1.89
(⫾ 0.84)
4.14*
(⫾ 0.66)
1.86
(⫾ 1.04)
4.36***
(⫾ 0.88)
3.46
(⫾ 0.88)
3.70
(⫾ 0.62)
3.08*
(⫾ 0.96)
3.15*
(⫾ 1.04)
2.48
(⫾ 0.85)
3.65
(⫾ 0.55)
3.48
(⫾ 0.88)
3.72
(⫾ 0.84)
4.08
(⫾ 0.79)
2.99
(⫾ 1.23)
4.03
(⫾ 0.53)
High
Low
⬍40 years ⬎40 years
Males
Females
Education (mean ⫾ SD)
Age (mean ⫾ SD)
Gender (mean ⫾ SD)
4.04
(⫾ 0.56)
0.79
Cronbach’s α Total sample
mean (SD)
Intervention questions
I think the intervention questions are
logical, easily readable, in good order,
easy to fill in and well-styled (7 items)
I think the intervention questions are
easily comprehensible (1 item)
I think there are too many intervention
questions (1 item)
Items/scales
1.86
(⫾ 0.89)
4.04
(⫾ 0.75)
2.01
(⫾ 1.10)
4.03**
(⫾ 1.19)
3.35*
(⫾ 0.89)
3.65
(⫾ 0.64)
3.01*
(⫾ 0.81)
2.88***
(⫾ 0.97)
2.48
(⫾ 0.83)
3.56
(⫾ 0.57)
3.45
(⫾ 0.92)
3.64
(⫾ 0.88)
4.10
(⫾ 0.85)
3.00
(⫾ 1.18)
4.03
(⫾ 0.54)
1
1.89
(⫾ 0.84)
4.11
(⫾ 0.68)
1.93
(⫾ 1.11)
4.41**
(⫾ 0.82)
3.72*
(⫾ 0.84)
3.80
(⫾ 0.53)
3.38*
(⫾ 0.96)
3.44***
(⫾ 0.99)
2.49
(⫾ 0.85)
3.72
(⫾ 0.58)
3.46
(⫾ 0.90)
3.72
(⫾ 0.82)
4.10
(⫾ 0.82)
2.84
(⫾ 1.28)
4.08
(⫾ 0.52)
2
2.04
(⫾ 0.70)
4.16
(⫾ 0.59)
1.67
(⫾ 0.71)
4.35**
(⫾ 0.79)
3.50*
(⫾ 0.67)
3.49
(⫾ 0.44)
2.97*
(⫾ 0.81)
3.51***
(⫾ 1.04)
2.20
(⫾ 0.74)
3.78
(⫾ 0.44)
3.60
(⫾ 0.68)
4.00
(⫾ 0.74)
3.92
(⫾ 0.93)
2.92
(⫾ 1.26)
3.99
(⫾ 0.67)
3
Stages of change (mean ⫾ SD)
1.95
(⫾ 0.93)
3.67***
(⫾ 0.89)
2.53***
(⫾ 1.24)
2.45****
(⫾ 0.83)
3.42
(⫾ 0.90)
3.63
(⫾ 0.53)
3.11
(⫾ 0.58)
3.29
(⫾ 1.00)
2.30
(⫾ 0.76)
3.64
(⫾ 0.56)
3.53
(⫾ 0.98)
3.62
(⫾ 0.97)
3.96
(⫾ 0.86)
2.06
(⫾ 1.06)
3.89
(⫾ 0.76)
Used to
1.89
(⫾ 0.81)
4.17***
(⫾ 0.61)
179***
(⫾ 0.95)
4.62****
(⫾ 0.48)
3.55
(⫾ 0.85)
3.7
(⫾ 0.58)
3.18
(⫾ 0.94)
3.22
(⫾ 1.05)
2.46
(⫾ 0.84)
3.67
(⫾ 0.55)
3.48
(⫾ 0.84)
3.77
(⫾ 0.81)
4.11
(⫾ 0.77)
2.98
(⫾ 1.26)
4.05
(⫾ 0.53)
Not used to
Computer use (mean ⫾ SD)
Table II. Mean scores (range 1–5) ⫾ SD for all items used in the acceptability and feasibility questionnaire according to gender, age, education, stages of change
and computer use
Acceptability and feasibility of a computer-tailored physical activity intervention
311
C. Vandelanotte and I. De Bourdeaudhuij
Differences in acceptability and feasibility
between groups
In general, very few significant differences were
found between gender, age groups, education
levels, familiarity with computer use and stages of
change, as also shown in Table II.
Only one significant gender-related difference
was found: men indicated to be more familiar
using a computer than women [t(188) ⫽ 3.06,
P ⬍ 0.01].
Participants over 40 years of age reported significantly more that the action plan was logical,
well-styled, comprehensible, correct and complete
compared to participants below 40 years of age
[t(117) ⫽ 2.01, P ⬍ 0.05]. Participants below the
age of 40 indicated that they were more familiar
with using a computer than those over the age of
40 [t(188) ⫽ 4.02, P ⬍ 0.001].
For stages of change, a significant difference
was found when respondents were asked if they
would use the physical activity advice [F(2,179) ⫽
7.37, P ⬍ 0.005]. Post hoc tests indicated that
precontemplators intended to use the physical
activity advice less than contemplators and preparators (P ⬍ 0.005), and those in the action and
maintenance stage (P ⬍ 0.05). Further, it was
found that precontemplators were significantly less
familiar with using computers than contemplators
and preparators (post hoc: P ⬍ 0.05), but not in
comparison with those in the action and maintenance stage [F(2,186) ⫽ 3.21, P ⬍ 0.05]. Finally,
two borderline significances were found. Precontemplators indicated less that the action plan was
useful, had added value on top of the physical
activity advice and would help to become more
active compared with the contemplators and preparators (post hoc: P ⬍ 0.1), there is no significant
difference between groups for those in the action
and maintenance stages [F(2,114) ⫽ 2.93, P ⫽
0.057]. The same goes for the credibility of the
action plan: precontemplators indicated less than
contemplators and preparators that the action plan
was credible (post hoc: P ⬍ 0.1), and again there
is no significant difference between groups for
those in the action and maintenance stage
[F(2,116) ⫽ 2.47, P ⫽ 0.089].
312
According to education, one significant difference was found—higher-educated participants
reported greater familiarity with using a computer
than lower-educated participants [t(188) ⫽ 2.86,
P ⬍ 0.05]. Further, three borderline significances
were found. Lower-educated participants intended
to use the physical activity advice more than
higher-educated participants [t(181) ⫽ 1.67, P ⫽
0.096]. Lower-educated participants also reported
more that the action plan was useful, added value
to the physical activity advice and would help
in becoming more active than higher-educated
participants do [t(116) ⫽ 1.90, P ⫽ 0.060]. Finally,
lower-educated participants reported less that the
computer program was user-friendly, clear and a
good choice for this intervention compared to
higher-educated participants [t(185) ⫽ 1.68, P ⫽
0.094].
Participants used to handling computers reported
being more familiar with using computers compared to subjects not used to handling computers
[t(188) ⫽ 20.22, P ⬍ 0.001]. They also indicated
more that the computer program was user friendly,
clear and a good choice for this intervention
compared to subjects not used to handling computers [t(185) ⫽ 3.82, P ⬍ 0.01]. They further
indicated less that they would prefer doing a written
test instead of a computer test compared to subjects
not used to handling computers [t(185) ⫽ 3.80,
P ⬍ 00.1].
Discussion
The aims of the present study were to test feasibility
and acceptability of a computer-tailored intervention promoting physical activity, and to explore
whether there were differences in the reported
feasibility and acceptability between different
stages of change, gender, age groups, education
levels and familiarity with computer use. Generally
speaking, the data showed that the tailored physical
activity intervention is a feasible and acceptable
tool for intervening in a general population. Participants reported having no problems with the
intervention questions and nearly all participants
accepted aspects related to appearance as well as
Acceptability and feasibility of a computer-tailored physical activity intervention
to content. However, more than a third of them
indicated that there were too many questions to
be answered before the physical activity advice
appeared. This is important to consider as it might
cause a number of participants to drop out before
reaching the end of the questionnaire and thus not
receive the health-enhancing feedback. The idea
that the questionnaire is too long and contains too
many questions is probably due to participants
having no idea how many questions they still have
to answer before receiving the advice. This may
have created a feeling of endlessness. According
to Steiner and Norman (Steiner and Norman, 1995),
this is a major disadvantage of a computerized
questionnaire. The problem might be solved by
eliminating less essential questions from the questionnaire and by adding a progression indicator to
the software. This gives participants an indication
of how many questions they still have to answer.
If computer-tailored interventions are put on the
Internet, it has to be emphasized to all participants
that they need to make a serious effort in order to
receive feedback. This is often a problem due to
the superficial nature of many Internet applications.
Most participants indicated that the physical
activity advice is interesting, logical, instructive,
comprehensible, well-styled and complete. However, a small difference was found between stages
of change—precontemplators were not as positive
about the advice compared to other stage groups.
Precontemplators are known to have high resistance against adoption of new health behaviors,
such as physical activity, and typically try to avoid
learning about their health problems (Prochaska
et al., 1994). Confrontation with new information
often results in denial and it is therefore possible
that they are not as positive about the physical
activity advice as participants in other stages. On
the other hand, stages of change had no effect on
the credibility of the advice. About two-thirds of
participants indicated that the physical activity
advice is credible, a fairly good result, considering
that about 40% of the participants are precontemplators. Further, only a small group of participants
indicated that the advice was too long, confusing
or that it gave too much information. Again, no
stages of change effects were noted. This may
indicate that the intervention tailors the stages
of change in the right way, presenting relevant
information in a good way to the right people.
Slightly more than 50% of all participants
reported that the physical activity advice was
relevant and useful, and 43% planed to use the
advice in the future. These percentages may seem
quite low, but we feel that these are not bad results.
Before receiving the advice, participants in the
precontemplation phase (40%) had no intention of
increasing their physical activity within 6 months.
After receiving the physical activity advice, it is
logical that a large proportion of them will still
have no immediate intention to increase their
physical activity and, consequently, will not plan
to use the advice. This was confirmed in our
analyses since precontemplators intended using the
physical activity advice significantly less than all
the other stages. This is also reflected in the
Stages of Change Theory (Prochaska et al., 1992).
However, this does not mean that the tailored
physical activity intervention had no effect on
precontemplators. The nature of this study does
not allow a change of stages to be demonstrated.
We might assume that participants in the action
and maintenance stage, because of their high levels
of activity, will also not intend to use the physical
activity advice. However, this was not the case.
Thus, it seems likely that participants in the action
and maintenance stage were more receptive to
health-enhancing information, rated its importance
higher (Dishman et al., 1985; Prochaska et al.,
1994) and therefore also reported an intention to
use the physical activity advice.
The literature describes very consistently that
physical activity increases with education level
(Sallis and Owen, 1999). In this respect it is
surprising that a trend was found that lowereducated participants intended to use the physical
activity advice more than higher-educated participants. An explanation might be that lower-educated
participants were less well informed (Sahler et al.,
1981; Sallis and Owen, 1999) and may have a
greater need for physical activity information,
compared to higher-educated participants. This is
313
C. Vandelanotte and I. De Bourdeaudhuij
a positive result as it shows that lower-educated
participants, who are traditionally more resistant
to health interventions (Dishman et al., 1985;
Prochaska et al., 1994), are willing to become
more active. This is consistent with a study (Brug
and Van Assema, 2000) evaluating the impact of
tailored dietary fat feedback. People with lower
education were more positive towards the tailored
intervention.
The reason why an action plan was included in
the intervention was to transform physical activity
intentions into specific acts. In general, participants
were positive about the appearance and content of
the action plan. In two acceptability studies of the
PACE materials (Long et al., 1996; Prochaska
et al., 2000) a ‘physical activity plan’ was evaluated
and comparable results were found. Most of their
participants also indicated having no problems
with the appearance and content of the physical
activity plan, they believed that the feedback suited
them well and the length of the program was good
for the majority of participants. However, in the
present study, participants over 40 years of age
reported significantly more that the plan was
logical, well-styled, comprehensible, correct and
complete compared to participants below 40 years
of age. It is possible that younger participants, being
more familiar with computers and the Internet,
were more demanding about the presentation of
information via the computer.
Normally, the completion of the action plan is
only meant for people motivated to change their
behavior. However, in the present acceptability
and feasibility testing, precontemplators were also
asked to fill in the action plan. This might explain
why credibility scores appeared to be mediocre
and why only a moderate number of participants
reported having the intention to use the action plan.
In the PACE studies (Long et al., 1996;
Prochaska et al., 2000), authors concluded that the
interactive ‘physical activity plan’ has potential
for increasing physical activity. Furthermore, the
PACE physical activity plan has already proved its
effectiveness and usefulness for increasing physical
activity (Long et al., 1996; Calfas et al., 1997).
However, in the PACE project, the physical activity
314
plan was the major intervention tool. In the present
study, the action plan was offered simultaneously
with extensive physical activity advice. As a consequence, it is unclear whether the action plan will
have an added value over and above the physical
activity advice. It could be that the action plan
itself has potential for increasing physical activity,
but not in combination with the physical activity
advice. An intervention study only using the action
plan should be conducted to clarify this matter.
Our finding that men, participants below the age
40 and higher-educated participants are significantly more familiar with using a computer is
consistent with the literature (Jurg and Zegwaart,
1995; Kehoe et al., 1998). Further, precontemplators were also found to be less familiar with
using a computer compared to contemplators and
preparators. This makes sense since proportionally
more women, participants over 40 years of age
and lower-educated people were in the precontemplation phase compared to the other stages.
In general, all the results concerning the use
of a computer suggested that this aspect of the
intervention is acceptable and feasible. Advantages
of computer-assisted administration, such as using
skip patterns (saving time and minimizing respondents’ burden) (Steiner and Norman, 1995) and
receiving feedback immediately (Kreuter et al.,
2000), are clearly appreciated.
On the whole, only a few significant differences
were found for stages of change, gender, age
groups, education levels and familiarity with computer. This leads to an important conclusion, i.e.
that this intervention tailored well for respondents
in different stages of change, for men as well as
for women, for younger and older participants, for
the higher and lower educated, and for those who
are or are not used to working with a computer.
This was one of the major aims when developing
the intervention. As mentioned earlier, the stages
of changes were considered in two ways when
writing feedback messages: (1) the content differed
between stages and (2) the way of approaching
participants also differed along stages. It seems that
this strategy succeeded in constructing feedback
tailored optimally to most participants, and prob-
Acceptability and feasibility of a computer-tailored physical activity intervention
ably explains why few differences in acceptability
and feasibility scores were found between groups.
There are several important limitations to note
in interpreting this study. First, the total sample
size was low and caution is therefore needed when
results are generalized to the Belgian population.
However, demographic distribution among the participants was comparable with the Belgian situation, indicating that our sample was representative
as far as sex, age and stages of change are
concerned. However, for computer use, a selection
bias was possible since this intervention is totally
computerized, making it more likely that respondents familiar with using a computer participated
(more than 80%). Second, as few researchers test
acceptability and feasibility of newly developed
interventions so extensively (and publish their
results), and since most interventions differ greatly
from each other, it is impossible to compare our
acceptability and feasibility results with previous
interventions. Third, since the acceptability and
feasibility questionnaire used in this study is a
self-assessment questionnaire, results depend on
the cooperation, subjectivity and honesty of participants.
Despite these limitations, our results allow us
to conclude that this computer-tailored intervention
is an acceptable and feasible tool for promoting
physical activity. All intervention elements proved
to be solid and usable. However, small adjustments
need to be made to further optimize the appearance
and content of the intervention. We also express
the need for more reports of research on the
acceptability and feasibility testing of computertailored interventions, since comparable studies
and interventions are scarce.
Acknowledgements
The authors would like to thank Elke Jongbloed
and Karen Meyns for their contribution in data
gathering, and the University of Maastricht for
supplying software. This study was financially
supported by the Ghent University and the Flemish
Fund for Scientific Research.
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Received on November 30, 2001; accepted on June 12, 2002
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