Randomized controlled trial of a brief research-based intervention promoting fruit and vegetable consumption.

Copyright © The British Psychological Society
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is prohibited without prior permission from the Society.
Randomized controlled trial of a brief
research-based intervention promoting
fruit and vegetable consumption
Ian Kellar1* and Charles Abraham2
1University of Cambridge
2University of Sussex, UK
Objective. The present study sought to test the efficacy of a brief research-based,
leaflet-like intervention to promote eating the recommended daily intake of fruit and
vegetables (RDIFV).
Design. A controlled, pre- post-test experimental study with random allocation and
a 1 week self-report behavioural follow-up was conducted.
Method. The intervention employed persuasive communication targeting
self-efficacy and intention, and invited participants to form implementation intentions
in relation to acquiring and preparing fruit and vegetables for consumption.
Results. Intervention participants had stronger post-intervention intentions to
consume the RDIFV, and higher anticipated regret in relation to failing to do so,
compared with controls, controlling for pre-intervention scores. At follow-up, the
intervention group was found to have eaten more fruit and vegetables and to have
consumed the RDIFV more frequently.
Discussion. It is concluded that this study supports the previously reported power
of implementation intentions to prompt enactment of intentions, and that a brief
research-based leaflet-like intervention could result in immediate enhancement of
intentions and anticipated regret, and promote greater fruit and vegetable
consumption.
Research suggests that daily consumption of fruit and vegetables reduces the risk of
cancer and cardiovascular diseases (Anderson, Hunt, Ford, & Finnigan, 1994; Block,
Patterson, & Subar, 1992) The World Heath Organization (1990) recommends
a minimum daily intake of 400 g, but only 20% of US adults (Serdula et al., 1995), and
only 3% of Scottish adults (Anderson et al., 1994) achieve this minimum. Moreover,
* Correspondence should be addressed to Ian Kellar, General Practice and Primary Care Research Unit, University of
Cambridge Institute of Public Health, Forvie Site, Robinson way, Cambridge, CB2 2SR (e-mail: IK261@medschl.cam.ac.uk).
The
British
Psychological
Society
543
British Journal of Health Psychology (2005), 10, 543–558
q 2005 The British Psychological Society
www.bpsjournals.co.uk
DOI:10.1348/135910705X42940

Copyright © The British Psychological Society
Unauthorised use and reproduction in any form (including the internet and other electronic means)
is prohibited without prior permission from the Society.
in the UK, a 5% decline in weekly fruit and vegetable intake was observed between 1992
and 1996 (Ministry of Agriculture, Fisheries, & Food, 1997).
National guidelines regarding the recommended daily consumption of fruit and
vegetables (RDIFV) vary (Cannon, 1992), but campaigns promoting consumption of five
or more pieces per day have been launched in the USA and European countries
(Benito et al., 1991; Brug, Debie, Vanassema, & Weijts, 1995; Netherlands Bureau for
Food & Nutrition Education, 1993; United States Dept. of Agriculture, Human Nutrition
Information Service, Dietary Guidelines Advisory Committee, & United States
Agricultural Research Service, 1995). Despite this investment, rigorous trials of
effectiveness are rare (Cox et al., 1998) and results have been described as mixed.
In a review, Rayner (1998) found that only 11%of interventions to have been successful in
raising fruit and vegetable consumption. Cox et al., suggest that this limited success may
be partially due to an underlying assumption that providing information is enough to
prompt behavioural change. Research in other areas of health promotion suggests that
campaigns that are most effective in changing attitudes and intentions in relation to
particular health behaviours aremore successful than campaigns that fail to promote such
cognitive antecedents (Jemmott& Jemmott, 2000). Thus, greater progressmight bemade
if interventions designed to promote consumption of the RDIFV focused on such
cognitive antecedents.
The theory of planned behaviour
The theory of planned behaviour (TPB; Ajzen, 1991) is the most widely applied model of
the cognitive antecedents of specific behaviours (Ajzen, 2002). Meta analytic studies
have demonstrated considerable empirical support for the model, which, on average,
accounts for 27% of the variance in behaviour across studies (Armitage & Conner, 2001).
The TPB proposes that behavioural intention and perceived behavioural control (PBC)
are the most proximal antecedents of action.
Intention represents the motivational antecedents of behaviour and indicates how
much effort a person is likely to devote to performing a behaviour (Ajzen, 1991).
PBC refers to the person’s appraisal of their ability to perform a behaviour, and is similar
to Bandura’s construct of self-efficacy (Ajzen, 1998; Bandura, 1998). The two constructs
are often strongly correlated, and meta-analytic evidence indicates that both are equally
strongly related to intentions and behaviour (Armitage & Conner, 2001). Both constructs
are thought to enhance intention, as people are more likely to intend to do things they
think they can accomplish. They may also impact on behaviour, when controlling for
intention, because greater confidence leads to enhanced effort and perseverance
(Bandura, 1977, 1998), and because perceived control reflects actual control (Ajzen,
1991).
At present, there is no consensus regarding the theoretical relationship between
self-efficacy and PBC or their distinct operationalizations. Research has indicated that
they refer to distinct sets of beliefs which have different effects on behaviour
(e.g. Manstead &Van Eekelen, 1998; Sparks, Guthrie, & Shepherd, 1997; Terry &O’Leary,
1995). For example, Terry (1993) argued that perceived self-efficacy refers to internal
barriers, such as a lack of skills, while PBC reflects perceptions of internal and external
factorswhichmay interferewith performance. This is consistentwith Schwarzer’s (1992)
suggestion that self-efficacymeasures are distinguished fromPBC items by self-references
(e.g. ‘easy forme’). However, Sparks et al.distinguish between the use of particular terms,
including ‘ease or difficulty’, measures of self-efficacy, and ‘control’ measures of PCB.
Other researchers have regarded the two constructs as synonymous (e.g. Godin & Kok,
Ian Kellar and Charles Abraham544

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1996). Ajzen (1988) has acknowledged that PBC was derived from Bandura’s work, and
Bandura (1998) has argued that self-efficacy encompasses PBC. Arguably, self-efficacy is a
more clearly defined construct than PBC (Armitage & Conner, 2001; Bandura, 1977)
referring to howeasy or difficult the person thinks itwill be for them to perform the target
behaviour successfully. Consequently, it may be easier to target self-efficacy, than PBC in
interventions, however, given the current state of this literature, it may be prudent to
operationalize and compare both constructs.
Intention is also determined by the person’s attitude towards the behaviour and
by their subjective norm (Ajzen, 1991; Fishbein & Ajzen, 1975). Attitudes refer to
the person’s overall evaluation of the behaviour; whereas subjective norm is based on the
person’s beliefs about what significant others think they should do. Thus, the TPB
proposes that a more positive attitude towards a behaviour (e.g. ‘eating four pieces of
vegetable and one piece of fruit every day during the next week would be good’),
a supportive subjective norm (e.g. ‘most people who are important to me think that
I should eating four pieces of vegetable and one piece of fruit every day during the next
week’), and greater PBC/self-efficacy (e.g. ‘forme, eating four pieces of vegetable and one
piece of fruit every day during the next week would be easy’), results in a stronger
behavioural intention (e.g. ‘I intend to eat four pieces of vegetable and one piece of fruit
every day during the nextweek’), which, togetherwith high PBC/self-efficacy, determine
the likelihood of that behaviour.
Ajzen (1991) has noted that the TPB could be extended if further constructs are found
to enhance the prediction of intention or behaviour, and Fishbein and Ajzen (1975)
acknowledge that the theory of reasoned action (and therefore the TPB) treats affect as an
undifferentiated aspect of the expectancy value model of attitude. Other theorists have
proposed that emotional experiences following a decision can influence motivation by
changing the subjective utilities of potential outcomes (e.g. Bell, 1982; Janis & Mann,
1977; Loomes & Sugden, 1982). Loomes and Sugden, for example, highlight the effects of
experiencing regret as a result of perceived discrepancies between ‘what is’ and ‘what
might have been’. Consequently, the impact of anticipated affect on decision-makingmay
be underestimated by the model of attitudes included in the TPB (van der Pligt,
Zeelenberg, van Dijk, de Vries, & Richard, 1998). Drawing upon the work of Janis and
Mann (1977), Richard, van der Pligt, and deVries (1995, 1996), and Richard, deVries, van
der Pligt (1998) have investigated the extent to whichmeasures of anticipated affect and,
in particular, anticipated regret (AR) enhance the level of prediction achieved by the TPB.
Subsequent research has revealed consistent evidence that AR measures increase the
capacity of the TPB to predict intentions. For example, Richard et al. (1998, 1995) found
that AR had a significant additional effect on behavioural expectations regarding
refraining from sexual intercourse and condom use. Similarly, Parker, Manstead, and
Stradling (1995) found that AR enhanced the prediction of intentions to commit driving
violations (e.g. cutting across other traffic). Richard et al. (1996) showed that AR
explained additional variance in expectations regarding eating junk food, using soft
drugs, and drinking alcohol, and Abraham and Sheeran (2004) found that AR helped to
account for strength of exercise intentions. Consequently, it seems plausible that AR
might also influence decisions regarding fruit and vegetable consumption.
The TPB has been applied to intending to, and eating, fruit and vegetables.
For example, Sparks and Shepherd (1992) found that the TPB accounted for 42% of the
variance in intentions to eat organic vegetables. Similarly, Brug, Lechner, and De Vries
(1995) found that the theory explained 47% of the variance in intentions to eat fruit, but
only 13% of the variance in intentions to eat boiled vegetables. Lien, Lytle, and Komro
Promoting fruit and vegetable consumption 545

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(2002) found that 31% of the variance in intentions to eat more fruit and vegetables,
but just 7% of the variance in frequency of fruit and vegetable consumption, was
explained by the model. However, Povey et al. (2000) found that 57.2% of the variance in
intentions to eat five portions of fruit and vegetables, and 56.7% of the variance in
self-reported fruit and vegetable intake was explained by the model. Thus, while further
work could clarify the predictive utility of the TPB as amodel of the cognitive antecedents
of fruit and vegetable consumption, available evidence suggests that the theoryprovides a
useful and parsimonious description of key motivational antecedents of this behaviour.
Enacting intentions
Intention is a powerful cognitive determinant of behaviour. Sheeran (2002), for
example, found that, across six studies of preventive health behaviour, the median
percentage of non-intenders who actually undertook the target behaviour was 7%.
However, intention is not a sufficient prerequisite of action. Sheeran also found that the
median percentage of intenders who did not act was 47%.
Goal theory (Austin & Vancouver, 1996) may help to clarify cognitive processes that
account for the ‘gap’ between intention and behaviour (Abraham & Sheeran, 2003;
Sheeran & Abraham, 2003). For example, goal theorists agree that many goals entail
sequences of hierarchically organized actions, so that envisaging and planning
instrumental or preparatory actions (Bagozzi, 1992; Sheeran, Abraham, & Orbell, 1999)
is crucial to the enactment of intentions. Eating fruit and vegetables involves acquiring
them, and, at least in the case of vegetables, preparing them for consumption in everyday
meals. Without such preparation it may be difficult or impossible to act on an intention
to eat the RDIFV. Consequently, as well as persuading people to intend to eat the RDIFV,
it seems likely that health promoters will need to facilitate planning of the preparation
necessary to reach this goal.
Gollwitzer and colleagues (Gollwitzer, 1993; Gollwitzer & Brandstatter, 1997) have
shown that intentions are more likely to be enacted if they are translated into
‘implementation intentions’, that specify when and where a particular action is to be
undertaken (Gollwitzer & Schaal, 1998; Sheeran, 2002). Gollwitzer’s studies have
shown that those who have formed implementation intentions are better able to recall
presented descriptions of the means to carry out an action, more likely to identify
environmental cues relevant to their planned action, and faster to initiate action in
response to situational opportunities. Thus, prompting implementation intention
formation in relation to preparatory actions necessary to act on an intention to eat the
RDIFV, could help people to translate intentions to eat the RDIFV into goal
achievement.
Verplanken and Faes (1999) provide support for the view that implementation
intention formation is a prerequisite to healthy eating. Participants in the experimental
condition of this study were asked to form implementation intentions by choosing a day
during the next 5 days on which they would eat healthily, and by specifying a menu for
that day, detailing what they would eat for breakfast, between breakfast and lunch, for
lunch, between lunch and dinner, for dinner, and after dinner. They were also asked to
keep an eating diary over the next 5 days. Control participants also kept an eating diary.
A dietician rated the degree of healthiness of participants’ eating over the specified
period and found a mean daily healthiness rating (out of 12) of 6.63 for the experimental
group, compared with 5.45 for the control group, Fð1; 98Þ ¼ 5:70, p , :05. The present
study sought to test the applicability of these ideas to promoting eating the RDIFV by
means of a brief intervention that could be widely distributed in leaflet form.
Ian Kellar and Charles Abraham546

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Targeting young people
Successful promotion of RDIFV must involve changing the eating behaviour of young
people, as current levels of consumption among 19- to 24-year-olds are the lowest of all
adult groups (Henderson, Gregory, & Swan, 2000). In the UK, one third of 18- to 19-year-
olds attend universities, and increasing efforts are being made to establish ‘health-
promoting universities’ (Tsouros, Dowding, & Dooris, 1996). Students are in the
process of establishing independent eating habits, and successful campaigns to promote
eating RDIFV amongst this population, together with school-based and worksite
programmes, could have population-level effects. The present study was undertaken in
the context of efforts to promote a healthier campus at a UK university.
The present study
The aim of the study was to design and evaluate a brief research-based, leaflet-like
intervention using a randomized trial. The intervention attempted to enhance
motivation and PBC/self-efficacy in relation to eating the RDIFV using persuasive
communication, and, drawing upon the work of Gollwitzer (Gollwitzer, 1999), invited
participants to formulate implementation intentions in relation to acquiring fruit and
vegetables and preparing meals that would enable consumption of the RDIFV.
Four hypotheses were tested: (1) an extended theory of planned behaviour,
including anticipated regret, will provide a good model of the cognitive antecedents of
fruit and vegetable consumption amongst students; (2) compared with (no leaflet)
controls, students who received the intervention will have higher scores on intention,
perceived behavioural control, self-efficacy and anticipated regret; (3) compared with
controls, students who received the intervention will eat more fruit and vegetables and
be more likely to eat the RDIFV; (4) behavioural differences between the control and
experimental groups (specified in Hypothesis 3) will be accounted for by differences in
relevant cognitions (specified by Hypothesis 2).
Methods
Participants and procedure
Data were collected from first- and second-year psychology undergraduates. To ensure
randomization, intervention and control booklets were placed in a single pile in an order
determined by a sequence of random integers generated by a true random number
generator (Haahr, 2004). The booklets from this single pile were then distributed in a
lecture theatre and laboratory class, and completed under examination conditions.
To reduce the risk of cross condition contamination, participants were explicitly
requested not to discuss the contents of their booklet with other participants over the
following week. No incentives were offered to first-year students; however, second-year
students gained course credit by participating. In total, 218 people completed the
booklet (115 first-year and 105 second-year students), of whom 15.6% were male and
84.4% were female. At follow-up, 146 people responded; 11.0% were male, and 89.0%
were female, with an overall attrition rate of 33%. Ages at follow-up ranged from 18 to 50
years (M ¼ 21:39).
Intervention, control task, and measures were piloted in undergraduate psychology
classes. Participants received a booklet which included: (a) pre-intervention measures,
(b) the intervention or control task, and (c) immediate post-intervention measures.
Booklets began with a definition of the RDIFV as follows: ‘the Recommended Daily
Intake of Fruit and Vegetables (RDIFV) for adults is four serving spoons of vegetables and
Promoting fruit and vegetable consumption 547