Prevalence of and intention to change dietary and physical activity
health risk behavioursq
Amelia S. Cooka,c,⇑, Fiona O’Learya, Tien Cheyb, Adrian Baumanb, Margaret Allman-Farinellia
aDepartment of Nutrition & Metabolism, School of Molecular Bioscience, The University of Sydney, Camperdown, Australia
bSchool of Public Health, The University of Sydney, Camperdown, Australia
cThe University of Sydney, Room 453, Molecular Bioscience Building GO8, Camperdown 2006, Australia
a r t i c l ei n f o
Received 21 February 2013
Received in revised form 13 July 2013
Accepted 28 July 2013
Available online 17 August 2013
Intention to change
Multiple health behaviors
Fruit and vegetables
a b s t r a c t
Poor nutrition and insufficient physical activity contribute to high rates of obesity. Prevalence, intention
to change and co-occurrence of four health risk behaviours (inadequate fruit and vegetables, excessive
dietary fat, excessive sugary beverages and inadequate physical activity in comparison to public health
recommendations) were investigated in an Australian population of working adults. Participants
(n = 105) completed sociodemographic and stage of change questionnaires. A subsample (n = 40) were
assessed twice to estimate test–retest repeatability. In the full sample, 73% were female, mean age
was 33.8 years and mean BMI was 23.8 kg/m2. Eighty-seven percent of participants consumed inade-
quate fruit and vegetables, 43% had excessive dietary fat, 42% had excessive sugary beverages and 29%
had inadequate physical activity. The proportions intending to change each behaviour were 57%, 25%,
18% and 24%, respectively. Two-thirds exhibited multiple risk behaviours and 38% intended to change
multiple risk behaviours. Fruit and vegetables and dietary fat were the most commonly paired risk
behaviours (39%) and the pair most intended to change (19%). Occurrence of multiple risk behaviours
was associated with being male (OR 3.10, 95% CI 1.06–9.03) or overweight/obese (OR 2.66, 95% CI
1.02–6.93). Targeting two risk behaviours, particularly fruit and vegetables and dietary fat, may be appro-
priate when designing health promotion programs in working populations.
? 2013 Elsevier Ltd. All rights reserved.
The contribution of non-communicable diseases to all deaths is
predicted to increase from 59% in 2002 to 69% in 2030, and
amongst the leading causes of death are ischaemic heart disease,
diabetes mellitus and hypertensive heart disease (projected rank-
ings for 2030 are 1, 7 and 11, respectively) (Mathers & Loncar,
2006). Rates of overweight and obesity are projected to rise up to
2.16 billion and 1.12 billion, respectively, by 2030 if past trends
continue (Kelly, Yang, Chen, Reynolds, & He, 2008). High blood
pressure, high body mass index (>21 kg/m2) and high cholesterol
was attributed with 7.6%, 7.5% and 6.2%, respectively of the total
burden of disease and injury in Australia in 2003 (Begg et al.,
2007). Dietary and physical activity behaviours that place individ-
uals at an increased risk of non-communicable diseases include a
diet low in fruit, vegetables, whole grains, fibre and polyunsatu-
rated fatty acids; a diet high in sugar-sweetened beverages, so-
dium, and trans and saturated fatty acids; and, a lifestyle with
high levels of physical inactivity and low physical activity (Lim
et al., 2012). They are a major contributor to mortality and morbid-
ity and are associated with type 2 diabetes, cardiovascular disease
and some cancers (Begg et al., 2007; McGinnis & Foege, 1993)
either directly or mediated via their contribution to risk factors
such as overweight and obesity, hypertension and high blood
In 2003, physical inactivity (defined as ‘no activity’) and inade-
quate fruit and vegetable intake accounted for 6.6% and 2.1% of the
total burden of disease and injury in Australia, respectively (Begg
et al., 2007) and ‘‘dietary risk behaviours and physical inactivity
collectively accounted for 10.0% of global disability-adjusted life
years internationally, in 2010’’ (Lim et al., 2012, p. 2224). While
trans and saturated fat intakes are not included in the burden of
disease analysis their adverse effect on blood cholesterol (Booker
& Mann, 2005; Van Horn et al., 2008) and endothelial function
(Fuentes et al., 2001) is well documented. Additional dietary fac-
tors contributing to increasing the risk of obesity, coronary heart
disease and type 2 diabetes include sugary beverage intake (de
Koning et al., 2012; Schulze et al., 2004).
0195-6663/$ - see front matter ? 2013 Elsevier Ltd. All rights reserved.
qConflict of interest: The authors declare they have no conflicting interests.
E-mail addresses: firstname.lastname@example.org (A.S. Cook), fiona.oleary@syd-
ney.edu.au (F. O’Leary), email@example.com (T. Chey), adrian.bauman@syd-
ney.edu.au (A. Bauman), firstname.lastname@example.org (M. Allman-
Appetite 71 (2013) 150–157
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journal homepage: www.elsevier.com/locate/appet
Furthermore, individuals with multiple health risk behaviours
are at a higher risk of cardiovascular disease, heart disease and dia-
betes (Fine, Philogene, Gramling, Coups, & Sinha, 2004; Pronk et al.,
Multiple health behaviour change interventions have the poten-
tial to direct limited healthcare resources in a cost-effective man-
ner to individuals at higher risk, and to maximise the reduction
of risks for, and prevalence of, largely preventable non-communi-
cable diseases (Prochaska, Spring, & Nigg, 2008). Therefore, an
understanding of which single health risk behaviours are most pre-
valent and commonly co-occur is indicated, to determine which
behaviours multiple health behaviour change interventions should
target. However, a person’s motivation to change will influence the
success of any health promotion program. The impact of this on
success of interventions is highlighted by Greene et al. (1999)
who states that approximately half of those not meeting recom-
mendations are precontemplators (typically resistant to change)
for whom traditional action-oriented programs will be less benefi-
cial than interventions specifically tailored to this stage (Greene
et al., 1999). Thus, an awareness of a populations’ overall level of
intention to change specific health risk behaviours is imperative
for designing successful health behaviour change interventions.
Workplaces have been highlighted as an important setting for
health promotion to improve lifestyle behaviours and reduce risk
factors (World Health Organization/World Economic Forum,
2008). In addition to health benefits, programs assist in organisa-
tional-level changes, such as alleviating the adverse effect of obes-
ity on sick leave (van Duijvenbode, Hoozemans, van Poppel, &
Proper, 2009), and may lead to a reduction in loss of productivity
or presenteeism, produce positive return on investment and re-
duce health care cost (Baker et al., 2008; Goetzel & Ozminkowski,
2008). To date, workplace-based health promotion programs have
demonstrated positive effects for increasing fruit and vegetable in-
take, reducing dietary fat and increasing physical activity (Conn,
Hafdahl, Cooper, Brown, & Lusk, 2009; Ni Mhurchu, Aston, & Jebb,
2010). Although very few of the studies included in the meta-anal-
ysis and systematic review were Australian (only 2%) it indicates
behaviours which interventions in Australian workplaces may also
have success improving. Based on the current evidence, ameliora-
tion of these three modifiable lifestyle behaviours, together with
sugary beverage intake, has the potential to reduce, or curb further
increases in, overweight and obesity and related non-communica-
ble diseases in workplace settings.
Therefore, the need to measure prevalence of, and intention to
change, these four behaviours in Australian workers is appropriate.
To our knowledge, this has not been examined in Australian work-
ers with exception for intention to change in dietary fat consump-
tion (Laforge, Velicer, Richmond, & Owen, 1999).
The purpose of this research was to determine the prevalence
of, and intention to change, these four key health risk behaviours
(fruit and vegetable intake, sugary beverage intake, dietary fat con-
sumption and physical activity) and the co-occurrence of these
behaviours in a working adult sample. The effect of sociodemo-
graphic variables was investigated to identify groups at higher risk.
The results of this study could be used to assess the need for inter-
ventions targeting this population. If demonstrated, the findings
could be used to inform health promotion programs for health
behaviour change among workers. In particular, to consider the
need for interventions tailored according to a populations’ overall
level of intention to change, to indicate how many and which
behaviours to target, and to identify which groups should be given
priority regarding intervention delivery.
A secondary aim was to investigate the test–retest reliability of
the study’s stage of change instruments in a subsample. The ratio-
nale for reliability testing is firstly, to inform program development
by considering participants’ consistency regarding their readiness
to change each behaviour and targeting behaviours that have stage
stability. Secondly, to ensure the instruments’ are reliable and var-
iability in participants’ stage between the repeated questionnaires
reflects only true stage transitions. This has not been investigated
in a similar population, within a short timeframe, with exception
for physical activity.
Participants (n = 105) were recruited from one Australian uni-
versity using posters on-campus, and written advertisements in-
cluded in online staff and student newsletters. Recruitment
material advertised for volunteers aged 18–60 years, defining
themselves as in full- or part-time paid employment. Due to
high rates of underemployment in part-time workers (21.7% in
2012; Australian Bureau of Statistics [ABS], 2013) we deemed
it important to include employees working as few as seven
hours per week. Those employed less than this were considered
Participants gave informed, written consent. Before they com-
pleted the study questionnaires, participants had their height and
weight measured in light clothing, without footwear by a research
dietitian. Height was measured using a portable stadiometer, to
the nearest 0.1 cm. Weight was measured to the nearest 0.1 kg,
using Tanita digital scales (Model HD-327). Weight was self-re-
ported for one participant who was pregnant and gave her pre-
pregnancy weight. Body Mass Index (BMI; kg/m2) was calculated
from these measures and participants were categorised into nor-
mal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese
This study was approved by The University of Sydney Human
Ethics Research Committee (approval number: 12962) in accor-
dance with The Code of Ethics of the World Medical Association
(Declaration of Helsinki).
Each participant completed a written sociodemographic ques-
tionnaire that included gender, date of birth, highest level of edu-
cation, income, postcode, country of birth and country of parents’
For each of the four different lifestyle behaviours (fruit and veg-
etable intake, sugary beverage intake, dietary fat consumption and
physical activity) a written stage of change questionnaire was
completed to determine readiness for change. Each was classified
according to five stages of change: precontemplation (a period in
which individuals not achieving the criterion behaviour are not
thinking about changing a behaviour, at least not within the next
6 months), contemplation (not achieving the criterion behaviour
but intending to change in the next 6 months), preparation (not
achieving the criterion behaviour but intending to change, usually
within the timeframe of the next month), action (achieving the cri-
terion behaviour but for less than 6 months) and maintenance
(achieving the criterion behaviour for at least 6 months) (Proch-
aska & Velicer, 1997).
Participants reported their intake for dietary behaviours and le-
vel of physical activity, and selected their level of intention to
change each behaviour, from pre-determined response options.
Essentially there were two types of questionnaires: a single-item
question with a 5-Choice response scale (for physical activity and
dietary fat) or a 4-item questionnaire (for sugary beverages and
fruit and vegetables) which included an initial quantitative compo-
nent to assess intake with respect to the criterion behaviour,
A.S. Cook et al./Appetite 71 (2013) 150–157
followed by three questions regarding length of time they had
spent at that current level of intake and their intention to change.
Each questionnaire included a complete definition, with exam-
ples, of the criterion behaviour. The four health risk behaviours’
criterion level were: (1) combined fruit and vegetable intake (P7
servings per day); (2) sugary beverage intake (<1 l per week); (3)
dietary fat consumption (‘‘consistently avoid eating high fat
foods’’); and (4) physical activity (P30 min per day, on average
over the last 2 weeks).
Some questionnaires were modified to conform to Australian
recommendations or to enhance the staging instrument by provid-
ing explicit definitions of the behaviour and criteria, and a clearer
description of the stages.
The stage of change questionnaire for fruit and vegetable intake
was modified from an instrument developed by the National Can-
cer Institute (Thompson & Byers, 1994) and later validated in U.S.
adults (Campbell et al., 1999). However, participants were staged
according to an adjusted score of seven serves of fruit and vegeta-
bles combined per day to meet the Australian adult recommenda-
tions: a minimum of two serves of fruit and five serves of
vegetables per day (National Health and Medical Research Council,
2003). The quantitative component asked: ‘‘How many serves of
fruit and vegetables do you eat each day? A serving equals a med-
ium-sized piece of fruit, ½ cup of cooked vegetables or 1 cup of sal-
ad vegetables. This includes fresh, dried, tinned and frozen fruit
and vegetables. It does NOT include fruit juice, fried potato or
The dietary fat consumption questionnaire was based on a
instrument, previously validated to detect dietary fat intake at
>vs. 630% of energy intake (Greene, Rossi, Reed, Willey, & Proch-
aska, 1994). It asked: ‘‘Do you consistently avoid eating high fat
foods? High fat foods are take away foods (such as fried fish or
chicken, hot chips, pizza, meat pies, sausage rolls), fried, battered
or crumbed foods, or high fat dairy foods (such as full cream milk
The sugary drink intake questionnaire has not yet been vali-
dated. The criterion behaviour (<1 l/week) was chosen due to rec-
ommendations that suggest ‘‘free sugars’’ should be limited to
<10% total energy intake (World Health Organization [WHO],
2003) in conjunction with a knowledge of current consumption
patterns (the food/beverage sources of, and percentage contribu-
tion to total energy intake from, added sugars in Australia and
the United States) (ABS, 1998; Marriott, Olsho, Hadden, & Connor,
2010). Current evidence supports this, suggesting that exceeding
as low as four cups (or 1 l) per week increases risk of total stroke,
particularly ischemic stroke, for women (Eshak et al., 2012). The
initial question asked: ‘‘On average, how many serves of sugary
drink do you have each week? A serve is 1 cup or 250 mL. Sugary
drinks include: cordials, soft drinks, fruit juices, energy drinks, fla-
voured mineral waters, sports drinks, mixer drinks and alco-
Physical activity was assessed using a modified version of a pre-
viously validated questionnaire (Marcus, Selby, Niaura, & Rossi,
1992). It was adjusted to focus on physical activity (specifically, lei-
sure-time and transport physical activity to control for individuals
whose occupation included mandatory physical activity) rather
than exercise alone and to reflect current Australian and interna-
tional adult recommendations: 30 min of moderate-intensity
physical activity on most, preferably all, days per week (Depart-
ment of Health and Ageing, 2005; WHO, 2010). A time frame of
‘‘the past two weeks’’ was used as an explicit measure of regularity
for performing this behaviour. It asked: ‘‘In the past 2 weeks, did
you do physical activity for an average of 30 min each day? Phys-
ical activity is walking or exercising for fitness, recreation or sport,
or walking for transport.’’ Regarding response options, to ensure
the preparation stage of change only captured participants intend-
ing to change in the very near future, a time referent of ‘‘the next
30 days’’ was specified. The resulting 5-Choice response scale
was: ‘‘YES, in the past 2 weeks, I have done physical activity for
an average of 30 min each day, and I have been doing this for MORE
than 6 months’’ (participants selecting this were classified as in
maintenance), ‘‘YES, in the past 2 weeks, I have done physical
activity for an average of 30 min each day, but I have been doing
this for LESS than 6 months’’ (classified as action), ‘‘NO, in the past
2 weeks, I have not done physical activity for an average of 30 min
each day, but I intend to in the next 30 days’’ (classified as prepa-
ration), ‘‘NO, in the past 2 weeks, I have not done physical activity
for an average of 30 min each day, but I intend to in the next
6 months’’ (classified as contemplation), and ‘‘NO, in the past
2 weeks, I have not done physical activity for an average of
30 min each day, and I do not intend to in the next 6 months’’ (clas-
sified as precontemplation).
Prevalence and intention to change
Participants defined as having a health risk behaviour (partici-
pants not achieving each behaviours’ criterion level; defined as
being in precontemplation, contemplation or preparation) were
further separated into those who did not intend to change (precon-
templation) and those who intended on changing in the next 1 or
6 month/s (preparation or contemplation, respectively).
Test–retest reliability of stage of change questionnaires
Staging questionnaires were administered twice in a subsample
of 40 participants to assess the test–retest reliability of the four
instruments. Spontaneous stage transitions can occur within a
short timeframe but are more likely if there is a lengthy delay
(Donovan, Jones, Holman, & Corti, 1998). Therefore, to minimise
the effect of real stage transitions on the reliability results, ques-
tionnaires were repeated within 6–8 days.
Prevalence of, and intention to change, each individual health
risk behaviour, and co-occurrence of behaviours (both overall
and those that participants’ intended to change) were determined
using descriptive statistics. Agreement between repeat measures
for test–retest reliability of the stage of change questionnaires
were assessed using weighted kappa (jw) statistics and were char-
acterised as: jw< 0.00, poor; jw= 0.00–0.20, slight; jw= 0.21–
0.40, fair; jw= 0.41–0.60, moderate; jw= 0.61–0.80, substantial;
and jw= 0.81–1.00, almost perfect (Landis & Koch, 1977). The per-
centage of those who changed stage (including the extent, direc-
tion and type of stage transition) between repeat assessments
was calculated. The effects of gender and BMI status (18.5–
24.9 kg/m2versus P25.0 kg/m2) on the likelihood of having each
of the individual health risk behaviours, and having two or more
health risk behaviours, was modeled using logistic regression
(PROC LOGISTIC). Gender and BMI effects on the likelihood of read-
iness to change each of the individual health risk behaviours, and
the likelihood of readiness to change two or more health risk
behaviours, was also modeled.
Statistical analyses were performed using Statistical Analysis
Systems software package version 9.2 (SAS Institute Inc., Cary,
NC, USA 2002–2008).
A.S. Cook et al./Appetite 71 (2013) 150–157
Sociodemographic and anthropometric characteristics
One hundred and five volunteers met the eligibility criteria.
Seventy-three percent were female, mean age was 33.8 years
(sd = 11.0), mean BMI was 23.8 kg/m2(sd = 3.4) and 32% were
overweight or obese. Almost half (49%) were born in Australia
but the majority of participants’ parents were born overseas. Par-
ticipants were of a high socioeconomic status, reflected by the
higher education level (73% had a degree or higher), income (31%
had a weekly income greater than $1300; median weekly income
was greater than $800) and negatively skewed distribution across
national deciles of relative advantage and disadvantage (86% were
in deciles nine or ten, corresponding to the most advantaged areas,
as measured by the postal area-based Socio-Economic Indexes for
Areas using the Index of Relative Socio-economic Advantage and
Disadvantage (ABS, 2008).
Individual health risk behaviours
The overall prevalence for the four health risk behaviours (using
the initial staging questionnaires) was, from highest to lowest:
fruit and vegetable intake (n = 91, 86.7%), dietary fat consumption
(n = 45, 42.9%), sugary beverage intake (n = 44, 41.9%), and physical
activity (n = 30, 28.6%).
However, if only participants not meeting the recommenda-
tions are considered, intention to change was found in 83%
(n = 25) of participants with inadequate physical activity (the
remaining n = 5, 17% were precontemplators); however, this was
the least prevalent behaviour. Whereas the corresponding figures
for the three dietary behaviours were lower: intention to change
was found in 66% (n = 60) of participants with inadequate fruit
and vegetable intake (n = 31, 34% in precontemplation), 58%
(n = 26) with excessive dietary fat consumption (n = 19, 42% in pre-
contemplation) and 43% (n = 19) with excessive sugary beverage
intake (n = 25, 57% in precontemplation).
The proportion of the total sample intending to change each
behaviour was, from highest to lowest: fruit and vegetable intake
(n = 60, 57.1%), dietary fat consumption (n = 26, 24.8%), physical
activity (n = 25, 23.8%), and sugary beverage intake (n = 19, 18.1%).
Multiple health risk behaviours
Figure 1 compares the prevalence of participants who exhibited
versus intended to change various numbers of health risk behav-
iours. The most commonnumber of health risk behavioursthat par-
ticipants reported was two behaviours (n = 33, 31.4%), slightly less
with one or three health risk behaviours (both n = 29, 27.6%), and
the least with zero or four health risk behaviours (both n = 7, 6.7%).
In participants with one or more health risk behaviours (n = 98,
93.3%), only 44 (44.9%) of these participants intended on changing
all the health risk behaviours they reported. Twenty-five partici-
pants (23.8%) exhibited a health risk behaviour/s but did not intend
on changing three or four health risk behaviours (n = 12, 11.4%). De-
spite the substantial difference between overall number of health
risk behaviours, and those that participants intended to change,
there were still numerous participants with two or more health risk
behaviours that they intended on changing (n = 40, 38.1%).
Co-occurrence of health risk behaviours
Table 1 shows the prevalence of paired health risk behaviours
overall and in participants intending to change both behaviours.
Fruit and vegetable intake combined with dietary fat consumption
was the most common paired health risk behaviour and the pair
most participants intended to change (n = 41, 39% and n = 20,
19% respectively). This was followed by fruit and vegetable intake
combined with sugary beverage intake: 36% (n = 38) exhibited, and
16% (n = 17) intended to change, this pair.
Co-variates of health risk behaviours
Males were 3.1 times more likely than females to have more
than one health risk behaviour (OR 3.10, 95% confidence interval
[CI] 1.06–9.03, n = 105). Participants with overweight or obesity
(BMI P 25.0 kg/m2) were 2.7 times more likely to have multiple
risk behaviours (compared to participants with a normal BMI:
Number of health risk behaviours
Number of health risk behaviours that
the participant intends on changing
Total number of participants with
0, 1, 2, 3 or 4 health risk behaviours (n)
Total number of participants intending to
change 0, 1, 2, 3 or 4 health risk behaviours (n)
Fig. 1. Participants’ total number of health risk behaviours compared with the
number of behaviours they intend to change (n = 105).
Dyads of four health risk behaviours (overall and according to intention to change)
(n = 105).
Paired health risk behaviourPrevalence of
Fruit and vegetablesc
Fruit and vegetablesc
Fruit and vegetablesc
aIncludes participants exhibiting the corresponding paired health risk behav-
iours (i.e. those categorised into precontemplation, contemplation or preparation
stages only, as those in action or maintenance are not exhibiting the health risk
bIncludes participants exhibiting and intending to change the corresponding
paired health risk behaviours (i.e. those categorised into contemplation or prepa-
ration stages only, as those in action or maintenance are not exhibiting the health
risk behaviour and participants in precontemplation do not intend to change in the
next 6 months).
cParticipants not achieving the criterion behaviour, defined as: <7 servings/day.
dParticipants not achieving the criterion behaviour, defined as: ‘‘not consistently
avoiding eating high fat foods’’.
eParticipants not achieving the criterion behaviour, defined as: P1 l/week.
fParticipants not achieving the criterion behaviour, defined as: <30 min/day.
A.S. Cook et al./Appetite 71 (2013) 150–157
18.5–24.9 kg/m2; OR 2.66, 95% CI 1.02–6.93, n = 105). Gender and
BMI had no effect on the occurrence of each individual health risk
behaviour, with exception of sugary beverages: males were 2.9
times more likely than females to be consuming excessive sugary
beverages (OR 2.86, 95% CI 1.17–6.98, n = 105). There was no effect
of gender or BMI on intention to change individual and multiple
Table 2 shows the test–retest reliability of the stage of change
questionnaires in a subsample (n = 40). Reliability was almost per-
fect for physical activity (jw= 0.84), substantial for fruit and vege-
table (jw= 0.71), and moderate for both sugary beverages
(jw= 0.60) and dietary fat (jw= 0.51). Gross misclassification
was highest in sugary beverages and dietary fat (both 20.0%).
Specific stage transitions that are likely to be false (not repre-
senting actual behaviour change) were lowest for physical activity
and fruit and vegetables (both 2.5%) and highest for sugary bever-
ages and dietary fat (15% and 12.5%, respectively) and are reported
as follows. In the physical activity stage of change questionnaire,
one regressed from maintenance to action. In the fruit and vegeta-
ble questionnaire, one participant transitioned from pre-action
(precontemplation, contemplation or preparation) to maintenance.
In the sugary beverages questionnaire, three participants transi-
tioned from pre-action to maintenance, one from maintenance to
action and two from maintenance to precontemplation. Discrep-
ancy of sugary beverage stage classification was generally evenly
distributed across stages but classification was most consistent
for maintenance and precontemplation. For the dietary fat ques-
tionnaire, we found a low level of congruence across most stages,
especially for precontemplation (which primarily leaked into the
maintenance stage; four participants transitioned from pre-action
to maintenance and one from maintenance to action). Across the
four behaviours, precontemplation and maintenance were the
most stable stages, which is consistent with the model’s premise
that participants in both do not intend to change (Prochaska & Vel-
Among participants who initially reported being in precontem-
plation, specific stage transitions that could represent genuine
variations in participants’ unwillingness to change, include move-
ment into contemplation, preparation or action. The proportion of
participants transitioning in this manner was 0.0% for physical
activity, 2.5% for fruit and vegetables, 7.5% for sugary beverages
and 10.0% for dietary fat. Among participants initially reporting
being ready to change (in contemplation or preparation), move-
ment into precontemplation or action could depict true inconsis-
tency regarding participants’ readiness to change. The proportion
of these specific stage transitions was 0.0% for dietary fat, 2.5%
for both sugary beverage and fruit and vegetable intake, and 5.0%
for physical activity.
This study aimed to investigate the prevalence of, intention to
change and co-occurrence of four key lifestyle behaviours and
the effect of sociodemographic variables. These results can be used
to ascertain need for intervention and if confirmed, to inform pro-
gram development. There was a high prevalence of the four indi-
vidual risk behaviours and multiple heath risk behaviours, with
two-thirds having two or more. For dietary behaviours, the propor-
tion of the sample population intending to change was substan-
tially lower than the overall prevalence but the extent of this
differed across individual behaviours. Nevertheless, about two in
five participants exhibited multiple behaviours that they intended
to change. Regarding prevalence and intention to change, the most
common behaviours were inadequate fruit and vegetable intake
and excessive dietary fat consumption (both as individual and
co-occurring behaviours). Multiple risk behaviours were more
common in males and overweight participants.
All four behaviours contribute to the burden of disease and are
highly prevalent in this sample. Thus, working adults are at a high
risk of non-communicable diseases, its associated morbidity and
absenteeism and presenteeism. A pressing need for health behav-
iour change interventions targeting workers and the workplace
environment appears indisputable.
Intention to change, as a proportion of the number not meeting
recommendations, varied across the four behaviours, indicating
substantial stage specificity. For example, the vast majority of par-
ticipants not meeting physical activity recommendations were in
contemplation or preparation, but most participants not meeting
sugary beverage recommendations were in precontemplation.
Compared with previous studies, the current proportions of partic-
ipants intending to change from those not achieving recommenda-
tions are higher. However, the overall order for the four behaviours
is consistent with previous findings (de Vries, Kremers, Smeets, &
Reubsaet, 2008; Glasson, Chapman, & James, 2010; Hu, Taylor,
Blow, & Cooper, 2011; Keller, Maddock, Hannöver, Thyrian, & Bas-
ler, 2008; Laforge et al., 1999). Since there is a substantial differ-
ence between the percentage not meeting recommendations and
the percentage intending to change (and this difference is variable
between behaviours), it is essential that needs assessments mea-
sure intention to change (not only prevalence) in all behaviours
studied. It also highlights important implications for the design
and delivery of health behaviour change interventions: firstly,
these findings show that for these four behaviours both groups
(precontemplators and those intending to change) require atten-
tion. Secondly, action-oriented health promotion programs (that
typically target people who are ready to change) must consider
not just overall prevalence of health risk behaviours, but also peo-
ples’ intention to change, by screening individuals’ stage of change
and focusing the delivery of these programs on only those who are
ready to change (since precontemplators are unlikely to benefit
from these interventions).
Test–retest reliability of stage of change questionnaires (n = 40).
BehaviourWeighted kappa coefficient [jw(95%
Exact agreement [n
aCI, confidence interval.
bParticipants classified one stage apart.
cParticipants classified Ptwo stages apart.
A.S. Cook et al./Appetite 71 (2013) 150–157
As previously reported in other populations (Berrigan, Dodd,
Troiano, Krebs-Smith, & Barbash, 2003; de Vries, Kremers, et al.,
2008; Hu et al., 2011; Keller et al., 2008; Reeves & Rafferty,
2005), this study indicated a high prevalence of coexisting multiple
health risk behaviours. Only 7% of participants meet all four behav-
iours’ recommendations, whereas 66% of participants exhibited at
least two health risk behaviours. These prevalence results signal a
need to address multiple health behaviours.
Debate continues surrounding whether health interventions
should target single or multiple health behaviours. Multiple behav-
iour change interventions may be a more appropriate use of re-
sources because they target individuals at a higher risk of having
poorer health outcomes (Australian Institute of Health and Welfare
[AIHW], 2005; Prochaska et al., 2008). However, single behaviour
change interventions may reduce the burden on the individual
and increase success in achieving the criterion behaviour (Proch-
aska & Sallis, 2004). There is some evidence that interventions tar-
geting a single behaviour can increase self-efficacy in achieving
behaviour change in a second behaviour and increase motivation
and readiness to change another poor behaviour that participants’
had previously not intended changing (Nigg & Long, 2012). Fur-
thermore, multiple behaviours may be improved by successfully
intervening on one behaviour which generates natural co-variation
in other behaviours, with or without receiving proactive treatment
(Prochaska, 2008). However, current results are contradictory
(Dutton, Napolitano, Whiteley, & Marcus, 2008; Johnson et al.,
2008). Evidence from interventions in individuals is inconclusive,
as few studies have compared single versus multiple behaviour
change and findings are inconsistent (Prochaska et al., 2008). Sim-
ilarly, the effectiveness of sequential versus simultaneous multiple
health behaviour change interventions remain unresolved, with
contradictory results thus far (Hyman, Pavlik, Taylor, Goodrick, &
Moye, 2007; Vandelanotte, Reeves, Brug, & De Bourdeaudhuij,
In a meta-analysis of workplace interventions, Hutchinson and
Wilson (2012) found larger effect sizes were associated with tar-
geting one area (diet or physical activity) rather than both (Hutch-
inson & Wilson, 2012). However, each study’s effect size was
calculated as a mean of all the dietary, physical activity and/or
other health outcomes measured rather than separating hard out-
comes (weight and/or clinical measures) from behavioural mea-
sures. Thus, the effectiveness on these two different types of
outcomes cannot be elucidated. Sweet and Fortier (2010) evalu-
ated the effectiveness of single versus multiple behaviour change
interventions on dietary and physical activity outcomes indepen-
dently from weight outcomes (Sweet & Fortier, 2010). They con-
cluded interventions focusing on a single behaviour were more
effective at improving that behaviour but targeting multiple
behaviours led to greater weight loss. However, both Hutchinson
and Wilson, and Sweet and Fortier did not consider change direc-
ted at multiple behaviours within an individual behavioural area,
as a multiple behaviour change intervention. It is unclear how
many truly single behaviours were targeted in these ‘single’ and
‘multiple’ behaviour change interventions. This limits the ability
of these studies to determine the effects of targeting varying num-
bers of behaviours, on health and behavioural outcomes. Indeed,
what constitutes a ‘single’ behaviour is not clearly defined. To
accurately judge the effectiveness of single versus multiple health
behaviour change interventions a more relevant and explicit defi-
nition of a single behaviour is required and needs to be used con-
sistently in evaluating interventions. We have interpreted a single
behaviour as one that has been given its own separate recommen-
dation from international or national health bodies or one that is
promoted in mass media as a specific behaviour to improve. This
reflects both the ability to perform the individual behaviour
distinctly from other behaviours, the recognisability of separate
components to a layperson, and its unique contribution to the bur-
den of disease. As such, diet is multi-factorial and the combination
of two or more dietary behaviours investigated here, has been
interpreted as a multiple behaviour (whereas leisure-time and
transport physical activity measured here, is interpreted as a single
If multiple health behaviour change interventions are favoured,
how many and which behaviours should an intervention aim to
improve? Overall prevalence, intention to change, capacity for pre-
vention or management of non-communicable diseases, maximisa-
tion of reach and relevance to participants, increased recruitment
and retention of participants, potential to tailor program contents
to the health risk behaviours an individual exhibits, whilst mini-
mising the burden and enhancing self-efficacy, are factors that
need to be considered when choosing how many and which behav-
iours an intervention should target.
We found a substantial difference between the number of
health risk behaviours an individual exhibited and the number
they were ready to change: 66% of participants had multiple health
risk behaviours however, few participants had three or four behav-
iours that they were willing to improve whereas, 38% intended to
change two or more behaviours. Therefore, in this population tar-
geting two health risk behaviours may be most appropriate for ac-
tion-oriented programs. This also ensures that higher risk groups
(those with multiple risk behaviours) are targeted, which increases
programs’ efficiency regarding the reduction of risk for obesity and
chronic disease. The most popular co-occurring paired risk behav-
iours were inadequate fruit and vegetable intake and excessive die-
tary fat, regarding both prevalence and intention to change. Other
researchers have also shown high prevalence and co-occurrence of
these two risk behaviours (Berrigan et al., 2003; de Vries, Kremers,
et al., 2008).
If an action-oriented program’s target behaviours and thus, con-
tent need to be ‘fixed’ for all participants, targeting fruit and vege-
tables, and dietary fat is justified. However, a flexible program
allowing a choice of two health risk behaviours that participants
are ready to change, from a wider range of options, would increase
program relevance to a greater number of people and broaden an
intervention’s reach (and thus, have a larger impact on reducing
the prevalence of non-communicable diseases). In this population,
only 19% wanted to change fruit and vegetable intake and dietary
fat consumption. However, double this (38%) had at least two
behaviours they intended on changing (that may have included
the other two behaviours: sugary beverage intake and physical
In participants with three or four health risk behaviours, chang-
ing all simultaneously may be too demanding or discouraging (de
Vries, van ’t Riet, et al., 2008). Limiting change to two behaviours,
prioritised on the basis on readiness to change and their likelihood
of contributing to impaired quality of life or mortality (Goetzel &
Ozminkowski, 2008) may be more realistic. If successful in chang-
ing their behaviour, the participant may have increased self-effi-
cacy to address another risk behaviour that perhaps they initially
had not intended to change (Prochaska et al., 2008).
Multiple health risk behaviours were more predominant in
males and in overweight or obese participants. However, they were
no more likely to want to change multiple risk behaviours or any of
the four individual behaviours. In particular, excessive sugary bev-
erage intake was more common in males, as previously reported
(O’Leary, Hattersley, King, & Allman-Farinelli, 2012), yet they were
mostly in precontemplation. Clearly campaigns to increase aware-
ness (for example, social marketing) are needed to move people in
these subgroups towards contemplating and preparing for change.
Test–retest reliability of staging instruments has ramifications
for interventions. If stage-matched interventions provide tailored
education to participants in the wrong stage (either due to stage
A.S. Cook et al./Appetite 71 (2013) 150–157
instability or an unreliable instrument) they could be ineffective or
unsatisfactory to participants.
It would be disadvantageous to target behaviours for which
there is a high likelihood that newly recruited participants’ readi-
ness to improve that behaviour could spontaneously change, with-
out this being attributable to the intervention (for example,
between intervention screening and delivery of the program). This
could render stage-matched interventions ineffective for some par-
ticipants and contribute to lower participant retention. Thus, eval-
uations of such interventions may underestimate its success.
Instead, stage-matched interventions should target behaviours
that exhibit stability in the stage that the intervention is being tai-
lored to. By investigating the extent of spontaneous and potentially
genuine stage transitions, consistency regarding participants’ read-
iness to change can be assessed. The current reliability results
show that the number of participants transitioning out of contem-
plation and preparation (to precontemplation or action) was low
across all four behaviours. Thus, for action-oriented interventions,
no behaviour should be avoided on the basis of stage stability.
There were higher proportions of participants transitioning out of
precontemplation (into contemplation, preparation or action) for
two behaviours: sugary beverage and dietary fat intake. However,
rather than suggesting interventions tailored to precontemplators
should preferentially target physical activity or fruit and vegetable
intake (due to less transitions), cautious interpretation is indicated.
The apparent instability of precontemplators (for sugary beverage
and dietary fat) may be inaccurate due to the likelihood that the
instruments for these behaviours are less reliable.
The stage of change instruments for physical activity and fruit
and vegetable intake demonstrated almost perfect to substantial
reliability but further refinement of the tools for dietary fat and
sugary beverage intake is indicated. In relation to the latter two
instruments, although the results may reflect spontaneous behav-
iour change rather than, or in addition to, the actual reliability of
the measurement tool (De Nooijer, Van Assema, De Vet, & Brug,
2005), the relatively high proportion of stage transitions, particu-
larly unrealistic transitions contrary to the model (for example,
from pre-action to maintenance or maintenance to action or pre-
contemplation, within 1 week), and the degree of gross misclassi-
fication, makes this unlikely. Valid and reliable assessment of
self-reported consumption of high fat foods may be hindered by
a lack of knowledge of the dietary fat recommendations and ability
to translate these into accurate judgements of foods they consume.
Instead, measurement of dietary fat requires a more tangible and
discrete behaviour that acts as an accurate proxy-measure of die-
tary fat intake. An explicit definition and criteria against which
an individual can more confidently judge and self-report their in-
take is needed. Similarly for sugary beverages, despite using a clear
definition and criteria, the reliability is a shortcoming. Validation of
the quantitative component of the questionnaire is indicated, as is
an increased understanding of the variability of sugary beverage
Study strengths and limitations
A key strength of the study is that it addresses the prevalence
and co-occurrence of health risk behaviours that participants’ in-
tend to change by measuring stage of change. As opposed to prev-
alence alone, this is a more useful guide in informing the design of
stage-matched or action-oriented health promotion interventions.
Although we advertised to full- and part-time workers, we did
allow those working as few as seven hours per week to participate.
As this might lower the mean working hours of the population
compared with Australian norms, the results may not be generali-
sable. Further limitations include a small sample of volunteers who
were of higher socioeconomic status, younger age, lower BMI,
mostly females, with many first-generation Australians. The prev-
alence of the four health risk behaviours was lower in this sample
than indicated by the recent National Health Survey (ABS, 2012).
While there is no data on dietary fat or sugary beverage intake,
the generalisability of the current study’s findings is likely limited.
Higher prevalence of poor dietary and physical activity behaviours
and nonadherence to a higher number of health recommendations
might be seen in populations with higher BMI and lower socioeco-
nomic status (AIHW, 2005; Miura, Giskes, & Turrell, 2009).
This study examines the prevalence and co-occurrence of four
key lifestyle-related behaviours to help plan interventions. Investi-
gating participants’ intention to change informs the development
of stage-matched interventions and is also recommended during
intervention screening due to stage specificity across various
health behaviours. In similar populations, developing interventions
targeting two behaviours (specifically, improving fruit and vegeta-
ble intake and dietary fat consumption) is appropriate for pro-
grams directed at both precontemplators and participants in the
contemplation and preparation stages. Males and those overweight
are identified subgroups to be targeted. Further research in multi-
ple health behaviour change interventions are required to deter-
mine the most effective strategy to assist in achieving both
outcomes: actual behaviour change and the reduction of risk fac-
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