What are colorectal cancer survivors’ preferences for dietary advice? A best-worst discrete choice experiment
What are colorectal cancer survivors' preferences for dietary advice? A best-worst discrete choice experiment
Stuart J. Wright 0 1 2 3 4 5
Debbie Gibson 0 1 2 3 4 5
Martin Eden 0 1 2 3 4 5
Simon Lal 0 1 2 3 4 5
Chris Todd 0 1 2 3 4 5
Andy Ness 0 1 2 3 4 5
Sorrel Burden 0 1 2 3 4 5
Sorrel Burden 0 1 2 3 4 5
0 School of Health Sciences, The University of Manchester , Jean McFarlane Building, Oxford Road, Manchester M13 9PL , UK
1 Manchester Academic Health Sciences Centre (MAHSC) , Manchester M13 9NT , UK
2 Manchester Centre for Health Economics, The University of Manchester , Manchester M13 9PL , UK
3 The NIHR Biomedical Research Unit in Nutrition, Diet and Lifestyle, University Hospitals Bristol NHS Foundation Trust and the University of Bristol , Bristol BS2 8AE , UK
4 Salford Royal NHS Foundation Trust , Salford M6 8HD , UK
5 School of Medical Science, The University of Manchester , Manchester M13 9PL , UK
Purpose Studies on healthy lifestyle interventions in survivors of colorectal cancer have been disappointing, demonstrating only modest changes. This study aims to quantify people's preferences for different aspects of dietary intervention. Method A best-worst discrete choice experiment was designed and incorporated into a questionnaire including participants' characteristics and a self-assessment of lifestyle. Results The response rate was 68% and 179 questionnaires were analysed. When analysing aggregate preferences, the modes of information provision selected as the most preferred were Bface-to-face^ (willingness to pay (WTP) £63.97, p ≤ 0.001) and Btelephone^ (WTP £62.36, p < 0.001) discussions whereas group discussions were preferred least (WTP − £118.96, p ≤ 0.001). Scenarios that included hospitals were most preferred (WTP £17.94, p = 0.031), and the favoured provider was bowel cancer nurses (WTP £75.11, p ≤ 0.001). When investigating preference heterogeneity, three subgroups were identified: Firstly, Btechnophiles^ preferring email (WTP £239.60, p ≤ 0.001) were male, were younger and had fewer risk factors. Secondly, a Bone-to-one^ group had strong preference for interventions over the telephone or at their local doctors and were older (WTP £642.13, p ≤ 0.001). Finally, a Bperson-centred^ group preferred faceto-face individual or group sessions (WTP £358.79, p < 0.001) and had a high risk lifestyle. Conclusion For survivors of colorectal cancer, there is not one approach that suits all when it comes to providing dietary advice. Implications for Cancer Survivors This is important information to consider when planning healthy lifestyle interventions which include dietary advice for survivors of colorectal cancer. Aligning services to individuals' preferences has the potential to improve patient experience and outcomes by increasing uptake of healthy lifestyle advice services and promoting a more tailored approach to dietary modifications, acknowledging sub-groups of people within the total population of colorectal cancer survivors.
Colorectal cancer; Survivorship; Information dietary; Discrete choice experiments; Conjoint experiments
Colorectal cancer is the third commonest cancer in men and
the second commonest in women . Cancer survival rates
are continually improving . Consequently, the healthcare
needs of people surviving colorectal cancer are becoming
increasingly important .
People who survive cancer have increased health needs
compared to controls [4–6]. In addition to this, Bpeople in
survivorship^ continue to have higher levels of health risks,
including a greater risk of cardiovascular disease, diabetes,
secondary malignancies and other cancers [7, 8]. This has
been shown to lead to decreased economic productivity
resulting from increased levels of overall morbidity and
ongoing disability associated with primary malignancy . It is
known that people who have survived cancer are motivated
to change their lifestyle, and this is termed a Bteachable
moment^ when it is ideal to intervene. .
Telephone interventions have been shown to improve
healthy eating and physical activity in people with colorectal
cancer in relation to exercise and body mass index (BMI),
although limited efficacy was demonstrated in terms of
improving fruit, fibre and alcohol intakes . Face-to-face
counselling combined with telephone support has been shown
to reduce body weight and BMI . Whilst benefits from
health promotion have been demonstrated in survivorship,
results have been limited to relatively modest reductions in
body weight and limited changes in dietary intake. Evidence
assessing the preferences of people with colorectal cancer to
guide service development is currently lacking. Aligning
services to people’s preferences to improve uptake is somewhat
intuitive but supported by the high dropout rates and the
number of participants who decline to take part in clinical trials
designed to evaluate a dietary intervention using a single
method of delivery [12, 13].
The current evidence base, although demonstrating some
positive effects of interventions [14, 15], has not been fully
implemented into long-term management or service delivery,
possibly due to lack of both cost-effectiveness data or
evidence of long-term benefits. Health promotion interventions
are recommended as part of colorectal cancer survivorship
care, but the specific recommendations on dietary
interventions for maintaining or achieving a healthy weight and eating
a diet high in fruit and vegetables are derived from evidence
based on expert opinion or case studies . Any systematic
attempt aimed at changing the lifestyle behaviour of
individuals is a complex intervention. Complexity in relation to these
interventions manifests in a number of ways including mode
of delivery, duration, targeted sample, professional providing
the intervention and place. Cognisant of the anticipated
challenges, guidance on the development and evaluation of
complex interventions has been developed .
Identifying patients’ preferences for the design of such an
intervention may help to engage service users and to increase
uptake. However, it is difficult to observe patients’ preferences
for healthcare interventions in practice. Traditionally
economic evaluation has relied on revealed preferences, drawing
inferences from how individuals act in a market. However,
given that healthcare is publicly provided in the UK, such
preferences cannot be observed. Discrete choice experiments
(DCEs), a type of stated preference study, have been used in
health service research to determine which aspects of
healthcare delivery are most valued by its users . In a
DCE, hypothetical healthcare services are described using a
set of pre-defined attributes and participants are asked to
choose which of the presented scenarios they would prefer
Design and analysis of DCEs is based on the proposition
that people choose goods or services based on their
preferences for individual characteristics of the goods .
Economic models can be used to quantify the relative strength
of preferences which participants have for different aspects of
a service based on their choices. There is a synergy between
the theoretical basis of DCEs and recommendations for the
evaluation of complex interventions. Medical Research
Council guidance highlights a need, during development of
new interventions, to identify Bthe active ingredients and how
they are exerting their effect^ .
A key advantage of DCEs is the ability to measure
preferences for the outcomes of an intervention and also for how it
will be delivered in practice. DCEs have been used for a
number of years in a variety of healthcare environments and have
now developed methodologically to include best-worst
scaling discrete choice experiments (BWDCEs) . In
BWDCEs, participants indicate their favourite and least
favourite options from a set of three or more scenarios, and
BWDCEs elicit more data from participants than a traditional
DCE without overburdening respondents [19, 21]. By
identifying how different groups of individuals vary in their
preferences, services can be further tailored to meet the demands of
patients. DCEs therefore have the potential to be an effective
tool in addressing two of the key issues in the evaluation of
complex interventions: they can assist with issues relating to a
lack of effect by evaluating implementation (or uptake) rather
than focusing on ineffectiveness and they can also determine
if Bstrict standardisation may be inappropriate^, with
adaptation of the interventions which may potentially lead to greater
The aim of this project was to identify and quantify
people’s preferences for different aspects of a diet-based lifestyle
intervention for people following treatment for colorectal
A questionnaire including a BWDCE (Fig. 1) was developed
and given to people who were recruited from colorectal
follow-up clinics between June 2015 and September 2015.
Research nurses recruited participants and provided them with
a paper version of the questionnaire and a postage paid
envelope to return the questionnaire to the research team.
Participants were included if they had completed treatment
Fig. 1 An example question with
different attributes and levels
Indirect cost to
you of receiving
GP practice nurse
for colorectal tumour, were over 18 years old, could
understand written English and could complete a questionnaire.
Those still in receipt of any anticancer therapy were excluded,
as were children and those who could not read or write
The BWDCE was combined with some questions to collect
data on participants’ characteristics including age, gender,
occupation, household income, marital status, site of surgery, centre,
ethnicity, smoking status, and number of healthy lifestyle criteria
that they followed based on international criteria . Participants
were asked to indicate their most and least preferred way of
receiving dietary information from a range of scenarios with a
number of attributes (Fig. 1). These were then ascribed an arbitrary cost
that enabled an economic value or willingness to pay (WTP) to be
attributed to each best or worst choice.
Attributes were developed from a previous set of
qualitative interviews with 32 participants who had survived
colorectal cancer . Semi-structured interviews were conducted
that asked participants where they received healthy lifestyle
information and their preferences about delivery including
place, format and personnel giving the information. These
were taken as the attributes for the BWDCE, and the levels
of each attribute were determined from the range of responses
given by the interview participants. This was complemented
by a systematic review of intervention trials undertaken with
people who had survived cancer . A group of cancer
survivors commented on the questionnaire prior to finalising the
structure and content.
The questionnaire included scenarios made up of different
attributes, and each attribute had a number of levels. The
experimental design software program NGene was used to
create a D-efficient design for the survey . Only one set of 12
choice questions was created as the use of four profiles in each
set and the two choices (best and worst) made by each
participant was believed to provide sufficient statistical strength to
the design without the need for blocking sets of questions. To
improve response efficiency to make all profiles realistic,
various combinations of levels were prevented from appearing
together. For example, telephone calls and emails could only
be received at home. An effective experimental design ensures
that the coefficients of interest can be precisely estimated.
Participants were asked to complete 12 choice sets in which
they had to choose their most and least preferred options from
a set of hypothetical diet-based interventions, with each set
containing four full profiles of attributes and levels. No
optout option (for example, receiving no information) was
included because of the large number of scenarios to be
evaluated. To understand how respondents completed the
questionnaire, questions were included to ascertain how difficult
participants found the task, whether they used all of the attributes
and levels or a sub-section to choose between alternatives, and
which attribute or level they found most important to them.
The DCE was initially piloted by 20 participants, and a
patient and public engagement group commented on the
questionnaire. Participants completed the experiment and then
discussed the content. No significant changes to the survey
were highlighted by the groups, and pilot but preliminary
analysis of the data suggested that too many restrictions to
the combinations of attributes and levels had been included.
As such, a new D-efficient design was created where
individuals could receive a discussion in their own home. This
allowed parameter estimates for all attributes and levels to
be identified in the full analysis.
A minimum sample size of 167 was determined necessary in
order to obtain reliable estimates of the coefficients of interest.
Details of the sample size calculations for this study can be
found in the technical supplementary material. Responses
were initially separated into best only and worst only and
analysed using conditional logistic regression models in the
statistical analysis software Stata 14 . The combined data
were then analysed using a sequential best-worst logistic
regression in Latent Gold Choice, which better facilitates the
analysis of best-worst data and latent choice models .
The inclusion of a cost attribute allowed for the calculation
of how much participants would be willing to pay for each
aspect of the intervention. The WTP values were calculated
using coefficients from regression models (see supplementary
material for WTP calculations).
Finally, preference heterogeneity was examined using
scale-adjusted latent class sequential best-worst logistic
regression analysis. This method of data analysis splits the
sample up into groups which have similar preferences for the
attributes and levels. The demographic information, which
was collected alongside the choice data, was then used to
identify the characteristics that predicted membership of
different categories. A scale-adjusted model  was used to
account for the fact that participants may have different levels
of consistency in their choices, a factor which can confound
preference estimates and make them difficult to compare.
A total of 265 participants were approached, and 190
questionnaires were returned from individuals attending six hospitals
throughout England. The study was registered on the Clinical
Research Network Portfolio for studies conducted in England;
therefore, hospitals were able volunteered to run the survey, and
each institution provided a research nurse to support recruitment.
During data cleaning, 11 responses were excluded due to missing
data, leaving 179 questionnaires for the final analysis (response
rate 68%). This relatively high response rate is in line with
findings which suggest that patients who have experience of a disease
may provide higher response rates to DCE questionnaires .
Participants’ characteristics are shown in Table 1; their mean age
was 68.9 (SD 8.6). The proportions of male and female
participants were similar (51 to 49%, respectively), and most
participants were married or living with a partner (66%). In this study, a
surprisingly large number of participants had no formal education
(36%), which may have implications for the generalisability of the
findings to people with different educational levels.
There were 70 (39%) participants who indicated that they
found the BWDCE to be easy, and 3 (2%) participants reported
finding the study to be very hard. When asked which attributes
participants used to make their choices, 143 (80%) stated that
they used the BHow^ attribute, 130 (73%) stated that they used
the BWhere^ attribute, 137 (77%) stated that they used the
BWho^ attribute and 52 (29%) stated that they used the cost
attribute. Respondents could indicate more than one attribute.
The modal choice of the most important attribute was BHow^,
Characteristics of the respondents
Living with partner 11(6.1)
Current smoker 99(55)
Never smoked 72(40)
Income per month
Under £250 4(2.2)
Over £2000 38(21)
Did not answer 67(37)
White British 170(94)
Bowel and rectum 17(9.4)
GCSE level 31(17)
A level or equivalent 20(11)
Higher degree 2(1)
Number of participants meeting healthy lifestyle recommendations
Healthy weight for height 113(63)
Moderate exercise at least 30 min a day 161(90)
5 pieces of fruit and vegetables 104(58)
Not having a lot of sweet food 130(73)
Not having a lot of food high in fat 155(86)
Having red meat <3 times a week 136(76)
Having <2 alcoholic drinks 112(62)
indicated by 77 (43%) participants, whilst BWho^ was also
perceived to be important, indicated by 64 (36%) participants. Only
2 participants stated that cost was the most important attribute.
Identification of best and worst aspects of a healthy
Conditional logistic regression of profiles which were chosen
as the best and worst in each choice task are shown in Table 2.
Participants favoured profiles in which information was
Best and worst case preferences for scenarios showing attributes and levels
Predictors of best scenarios
Predictors of worst scenarios
Level of agreement
WTP willingness to pay
*p ≤ 0.05, **p ≤ 0.005, ***p ≤ 0.001
provided via either telephone calls (p < 0.001) or individual
face-to-face discussions (p < 0.001). They preferred to receive
this information in hospitals (p = 0.031) and for specialist
bowel cancer nurses to provide the information (p < 0.001).
Group discussions (p < 0.001), information provided in
community centres (p < 0.001) and information provided by
general practice nurses (p < 0.001) were all levels that were
disliked by participants.
Individual discussions were unlikely to be chosen as
the worst way of receiving information. This seems
congruent with findings of the best case scenario analysis in
that this mode of information provision was not associated
with an increased likelihood of a profile being chosen as
the best. The level of apparent (dis)agreement between the
separate analyses of best and worst options is detailed in
Table 2. Participants’ disliking of community centres and
general practice nurses along with their preference for
information from bowel cancer nurses echoes the findings of
the best case analysis. However, receiving information in a
hospital was found to increase the chance of a profile
being selected as worst. This level also tended to appear
in profiles which were selected as best, presenting
ostensibly conflicting evidence. Other apparent contradictions
included preferences for telephone discussions, group
discussions and receiving information in a patients’ own
home, although in each case, one of the WTP values
generated in the best or worst case analysis was statistically
insignificant. These examples of counterintuitive findings
are indicative of preference heterogeneity in the group.
Sequential best-worst case analysis
The results of the sequential best-worst logistic regression are
presented in Table 3. Telephone calls and individual
discussion were the most preferred methods of receiving information
with group discussions and emails being disliked. Participants
wanted to receive information at their general practice (GP) or
in their own home and not in a community centre. The value
for information received in a hospital may again indicate
varying preferences for a hospital-based intervention. Participants
overwhelmingly wanted information from a specialised bowel
cancer nurse rather than a dietician or a general practice nurse.
Profiles with higher costs were preferred less, as would be
expected. WTP values are included to indicate how valuable
the different attributes and levels are relative to the mean value
of healthy lifestyle and dietary advice.
Determining categories using latent class analysis
A scale-adjusted latent class sequential best-worst logistic
regression was used to account for heterogeneity within
preferences by identifying groups of participants with similar
preferences (see supplementary material for further details).
The results of the scale-adjusted latent class sequential
best-worst logistic regression are presented in Table 4. The
key difference between the three categories was in their
preferences for how they received advice. The first category
(n = 79, 44%), Btechnophiles^, preferred indirect
communication and was the only group who valued receiving information
Sequential best-worst logistic regression
WTP willingness to pay
*p ≤ 0.050, **p ≤ 0.005, ***p ≤ 0.001
by email. They were willing to pay an additional £239.60
relative to the average for a dietary intervention. They also
valued receiving dietary advice via telephone or in an
individual discussion. This group preferred to visit their GP to receive
the information or to receive it in their own home and did not
want to travel to a hospital or a community centre. Members
of the technophiles group were more likely to be younger, to
be male and to have low self-reported risk.
The second category (n = 61, 34%) had preferences
indicating they preferred Bone-to-one^ communication. This was
exhibited in this group’s strong preferences for receiving
information over the telephone (WTP £642.13) or in an
individual discussion (WTP £594.32). Members of this group were
averse to receiving information via email (WTP −£1350.51).
This group preferred receiving dietary advice at their GP
surgery and did not want to receive it at a community centre. The
older participants were the more likely to fall into the
The final category (n = 39, 22%) was labelled the
Bpersoncentred^ communicators, who valued direct in-person dietary
advice. They value being able to receive healthy lifestyle
advice face-to-face, whether this was via individual discussion
(WTP £358.79) or group discussion (WTP £290.62).
Receiving information via the indirect methods, telephone
calls and emails, was strongly disliked by this group.
Despite having strong opinions about how they would prefer
to receive information, the Bperson-centred^ group did not
have strong preferences about where they wanted to receive
information. Members of this group were more likely to have
high self-reported risk and were more likely to be female.
Other demographic factors including education, years of
education, ethnicity, site of surgery, smoking status,
recruitment site and whether the patient had a stoma did not predict
membership of the groups.
Across all groups, there was a universal preference for
information to be provided by a specialist bowel cancer nurse.
Participants were generally against or indifferent to receiving
advice from a dietician. All groups preferred not to receive
healthy lifestyle advice from a general practice nurse.
The use of a BWDCE has highlighted some novel findings
regarding preferences for dietary advice after treatment for
people who have survived colorectal cancer. Exploration of
heterogeneity within the cohort has identified different
preferences for lifestyle advice in groups who have similar
characteristics. The identification of sub-groups within the total
colorectal cancer survivorship population adds to the evidence
base. This experiment has shown people who require dietary
intervention and who regard themselves as most at risk would
prefer face-to-face advice with a specialist bowel cancer nurse
at a hospital (Bperson-centred^ group). However, younger
males who indicated they were adhering to current guidelines
stated they would prefer information in their own home using
email (Btechnophiles^). Older people were more likely to
favour telephone contact or face-to-face consultations at their
GP surgery (Bone-to-one^ group). The data provide evidence
that for healthy lifestyle specifically relating to dietary
interventions, there is not one style that is suitable for all people.
The use of the BWDCE method allowed twice the amount
of data to be captured compared to a traditional DCE, leading
to increased statistical precision in the parameter estimates. At
an aggregate level, information provided in a group discussion
or via email was disliked by participants, albeit there was
heterogeneity between groups. The inconsistencies apparent
between participants’ choices for the best and worst
interventions in a choice set provide an indication that preference
heterogeneity may exist with regard to the provision of dietary
Previous attempts to design and evaluate intervention
services for people surviving cancer have relied on a universal
approach. However, to date, such approaches have resulted in
only modest changes to high-risk health behaviours
particularly in people with colorectal cancer . As a result, the
effectiveness and cost-effectiveness of such interventions in
preventing recurrence of cancer appears limited.
Scale-adjusted latent class sequential best-worst logistic regression
Group 1: Btechnophiles^ (n = 44%) Group 2: Bone-to-one^ (n = 34%) Group 3: Bperson-centred^
(n = 22%)
Coefficient p value
Coefficient p value WTP
WTP willingness to pay
*p ≤ 0.05, **p ≤ 0a.005, ***p ≤ 0.001
One potential cause of this limited impact may be the
presence of heterogeneity with regard to people’s preferences for
receiving dietary interventions. This study suggested that
there may be a range of categories of participants with
different preferences for how they receive information. Participants’
self-reported adherence to healthy lifestyle recommendations
was one of the key predictors of membership in these
categories. Those with adherence to fewer recommendations had a
higher risk associated with future malignancies and were a
category of participants who prefer information to be
delivered in face-to-face individual or group discussions. On the
other hand, people who followed more of the healthy lifestyle
recommendations were more likely to belong to a category of
people preferring information via telephone calls, emails and
Following on from the results, it would seem that the best
approach to provide dietary advice may therefore be to tailor
information to different groups of people based on their
preferences. Groups who adhered to fewer healthy lifestyle
recommendations exhibiting more risky behaviours
potentially have a greater risk of further malignancy. Following a
greater number of healthy lifestyle recommendations has been
associated with a lower hazard ratio of dying from cancer,
circulatory disease or respiratory disease . This group would
therefore be most likely to benefit from healthy lifestyle and
dietary advice. However, this study suggested that their
preference for face-to-face discussion is not being met by the
current trend for telephone-based information. Failing to
engage this specific group in behavioural change may present a
Furthermore, the preference of individuals with a lower risk
for an intervention via telephone calls or emails may present a
more cost-effective way of delivering an intervention. These
modes of information provision are likely to be less costly to
healthcare providers and target people who are less likely to
benefit from behavioural change. Personalisation of healthy
lifestyle advice may therefore facilitate the more efficient use
of healthcare resources. In order to fully evaluate the most
cost-effective manner of providing healthy lifestyle advice, an
economic evaluation building on a clinical trial would be
required. In this way, the potential benefits gained from
personalising lifestyle information in terms of reducing cancer
recurrence could be systematically compared to the additional
costs which may be incurred.
There were some limitations to this study. Firstly, there
were a significant number of participants in this study who
had no formal education. This may mean that the sample used
in this study was not representative of the wider patient
population and that the conclusions of this paper should be
interpreted with care. However, in the latent class analysis,
educational level was not found to be a predictor of how
patients preferred to receive information.
Furthermore, the WTP values calculated from analysis of
best and worst choices separately differ significantly. If best
choices are the opposite of worst choices, as is assumed in the
combined models, these values should be equal. This problem
has been commonly observed in previous best-worst scaling
experiments . Some of this effect may be due to
heterogeneity in participants’ best and worst choices. For example, the
contradiction in WTP values for telephone calls and group
discussions can be explained by the assumption that some
groups of people prefer these modes whilst others dislike
them. It has also been suggested that participants in
bestworst discrete choice experiments may use more simplistic
methods when making worst choices. For example,
participants may always choose profiles with a given level of an
attribute as worst. This may be the case with the email level,
which was chosen as worst by almost all participants other
than younger males. This may indicate that the participants
were not fully evaluating each profile. DCEs are grounded on
theories of rational consumer choice. As with any applied
choice experiment, it is difficult to ascertain the extent to
which participant responses adhere to assumptions of
economic theory. Systematic differences in how participants
chose best and worst profiles cannot be entirely ruled out in
attempts to explain apparent inconsistencies in WTP values.
The key findings of this preference-based study were that
different preferences are expressed by people who have
survived colorectal cancer regarding the delivery of healthy
eating intervention. Factors to take into consideration are age,
self-reported lifestyle behaviours and gender. At an aggregate
level, preferences were for a dietary intervention delivered by
a bowel specialist nurse, locally and by an individual
discussion either face-to-face or on the telephone. However, the
additional data provided by the best-worst methodology has
allowed categories of people to be highlighted that would be
more likely to prefer alternative delivery of dietary
information either by email or group sessions. The key to
determining the likelihood of preferences was the individual’s
age, gender and self-assessed risk based on healthy lifestyle
recommendations for the prevention of cancer . The three
groups, Btechnophiles^, Bone to one^ and Bperson-centred^
communicators, are characterised by preferences based on
mode of delivering information, place and professional, and
this is influenced by their overall risk, age and gender. This
information can be used in future when designing
interventions to ensure the right individuals are targeted by the right
approach since clearly Bone size does not fit all^.
BMI, body mass index; BWDCE, best-worst scaling
discrete choice experiments; DCEs, discrete choice experiments;
GP, general practitioner; SD, standard deviation; WTP,
willingness to pay.
Acknowledgements The authors would like to thank the participants
for taking part in the DCE.
Authors’ contributions SB, SL, AN and CT devised the project. DG
collected the data, managed the spreadsheet and undertook the descriptive
statistics. SB, DG, SW and ME designed the DCE. SW and ME had
previous experience of BWDCEs and had received specific training in
this methodology. AN, CT, SB and SL assisted with the design of the
questionnaire. SW and ME advised on the design of the experiment, and
SW undertook the analysis. SW and SB wrote the manuscript, and all
authors inputted in to the process and have approved the final manuscript.
Compliance with ethical standards
Competing interests The authors declare that they have no competing
Funding The study was funded as part of a Senior Clinical Lectureship
on the Clinical Academics Training programme, National Institute of
Health Research, UK.
Ethics approval and consent to participate Ethics approval was
gained from NRES Committee London - South East (Research Ethics
Committee reference 15/LO/0399). A patient information sheet was
provided, and participant consent was assumed on the receipt of a completed
Availability of data and material These data are not publically
available, but investigators who wish to access specific parts of the data should
contact the corresponding author.
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1. Ferlay J , et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer . 2010 ; 127 ( 12 ): 2893 - 917 .
2. Maddams J , et al. Cancer prevalence in the United Kingdom: estimates for 2008 . Br J Cancer . 2009 ; 101 ( 3 ): 541 - 7 .
Maddams J , Utley M , Moller H. Projections of cancer prevalence in the United Kingdom , 2010 - 2040 . Br J Cancer . 2012 ; 107 ( 7 ): 1195 - 202 .
Khan NF , et al. Consulting and prescribing behaviour for anxiety and depression in long-term survivors of cancer in the UK . Eur J Cancer . 2010 ; 46 ( 18 ): 3339 - 44 .
Heins MJ , et al. For which health problems do cancer survivors visit their general practitioner ? Eur J Cancer . 2013 ; 49 ( 1 ): 211 - 8 .
Nobbs HM , et al. Do dietary patterns in older age influence the development of cancer and cardiovascular disease: a longitudinal study of ageing . Clin Nutr . 2016 ; 35 ( 2 ): 528 - 35 .
Evans HS , et al. The risk of subsequent primary cancers after colorectal cancer in southeast England . Gut. 2002 ; 50 ( 5 ): 647 - 52 .
Demark-Wahnefried W , et al. Riding the crest of the teachable moment: promoting long-term health after the diagnosis of cancer . J Clin Oncol . 2005 ; 23 ( 24 ): 5814 - 30 .
Miller P , et al. Dietary supplement use among elderly, long-term cancer survivors . J Cancer Surviv . 2008 ; 2 ( 3 ): 138 - 48 .
Hawkes AL , et al. Effects of a telephone-delivered multiple health behavior change intervention (CanChange) on health and behavioral outcomes in survivors of colorectal cancer: a randomized controlled trial . J Clin Oncol . 2013 ; 31 ( 18 ): 2313 - 21 .
2014 ;348 Finocchiaro C , et al. Effect of specific educational program on dietary change and weight loss in breast-cancer survivors . Clin Nutr . 2016 ; 35 ( 4 ): 864 - 70 .
Kushi LDC , McCullough M , Rock CR , Demark-Wahnefried W , Bandera EV , Gapstur E , Patel AV , Andrews K , Gansler T , The 17 .
American Cancer Society 2010 Nutrition and Physical Activity Guidelines Advisory Committee. American Cancer Society Guidelines on nutrition and physical activity for cancer prevention: reducing the risk of cancer with healthy food choices and physical activity . CA Cancer J Clin . 2012 ; 62 : 30 - 67 .
Craig P , et al. Developing and evaluating complex interventions: the new Medical Research Council guidance. Br Med J . 2008 ; 337 : 1 - 6 .
Clark MD , et al. Discrete choice experiments in health economics: a review of the literature . PharmacoEconomics . 2014 ; 32 ( 9 ): 883 - 902 .
Lancsar E , et al. Best worst discrete choice experiments in health: methods and an application . Soc Sci Med . 2013 ; 76 : 74 - 82 .
Flynn T. Valuing citizen and patient preferences in health: recent developments in three types of best-worst scaling . Expert Rev Pharmacoecon Outcomes Res . 2010 : 10 ( 3 ).
Flynn TN , et al. Best-worst scaling: what it can do for health care research and how to do it . J Health Econ . 2007 ; 26 ( 1 ): 171 - 89 .
Burden ST , et al. An exploration of food and the lived experience of individuals after treatment for colorectal cancer using a phenomenological approach . J Hum Nutr Diet . 2016 ; 29 ( 2 ): 137 - 45 .
Dietary interventions for adult cancer survivors . Cochrane Database of Systematic Reviews Issue 9 . Art . No.: CD011287 , 2014 ( 9 ). doi: 10.1002/14651858.CD011287.
ChoiceMetrics, NGene. 2014 .
StataCorp , Stata 14 . 2016 .
Statistical Innovations , Latent Gold Choice 5 .1. 2016 .
Burke P , et al. The scale-adjusted latent class model: application to museum visitation . Tour Anal . 2010 ; 15 : 147 - 65 .
Watson V , Becker F , de Bekker-Grob E. Discrete choice experiment response rates: a meta-analysis . Health Econ . 2016 . doi:10.1002/ hec.3354.
Vergnaud A-C , et al. Adherence to the World Cancer Research Fund/American Institute for Cancer Research guidelines and risk of death in Europe: results from the European Prospective Investigation into Nutrition and Cancer cohort study . Am J Clin Nutr . 2013 ; 97 ( 5 ): 1107 - 20 .