Independent contributors to overall quality of life in people with advanced cancer

Abstract

Background:
The definition of health for people with cancer is not focused solely on the physiology of illness and the length of life remaining, but is also concerned with improving the well-being and the quality of the life (QOL) remaining to be lived. This study aimed to identify the constructs most associated with QOL in people with advanced cancer.

Methods:
Two hundred three persons with recent diagnoses of different advanced cancers were evaluated with 65 variables representing individual and environmental factors, biological factors, symptoms, function, general health perceptions and overall QOL at diagnosis. Three independent stepwise multiple linear regressions identified the most important contributors to overall QOL. R(2) ranking and effect sizes were estimated and averaged by construct.

Results:
The most important contributor of overall QOL for people recently diagnosed with advanced cancer was social support. It was followed by general health perceptions, energy, social function, psychological function and physical function.

Conclusions:
We used effect sizes to summarise multiple multivariate linear regressions for a more manageable and clinically interpretable picture. The findings emphasise the importance of incorporating the assessment and treatment of relevant symptoms, functions and social support in people recently diagnosed with advanced cancer as part of their clinical care.

Full text

Independent contributors to overall quality
of life in people with advanced cancer
A M Rodrı
´guez*
,1
, N E Mayo
1,2,3
and B Gagnon
4
1
Faculty of Medicine, School of Rehabilitation Sciences, McGill University, 3654 Prom Sir William Osler, Montreal, Quebec, Canada
H3G 1Y5;
2
Department of Medicine, Canadian Academy of Health Sciences, McGill University, Montreal, Quebec, Canada;
3
Division of Clinical Epidemiology and Geriatrics, McGill University Health Center, Royal Victoria Hospital Site, Ross Pavilion R4.29,
687 Pine Avenue W, Montreal, Quebec, Canada H3A 1A1 and
4
Department of Medicine and Oncology, McGill University,
McGill University Health Center, Ross Pavilion R4.29, 687 Pine Avenue West, Montreal, Quebec, Canada H3A 1A1
Background: The definition of health for people with cancer is not focused solely on the physiology of illness and the length of life
remaining, but is also concerned with improving the well-being and the quality of the life (QOL) remaining to be lived. This study
aimed to identify the constructs most associated with QOL in people with advanced cancer.
Methods: Two hundred three persons with recent diagnoses of different advanced cancers were evaluated with 65 variables
representing individual and environmental factors, biological factors, symptoms, function, general health perceptions and overall
QOL at diagnosis. Three independent stepwise multiple linear regressions identified the most important contributors to overall
QOL. R
2
ranking and effect sizes were estimated and averaged by construct.
Results: The most important contributor of overall QOL for people recently diagnosed with advanced cancer was social support. It
was followed by general health perceptions, energy, social function, psychological function and physical function.
Conclusions: We used effect sizes to summarise multiple multivariate linear regressions for a more manageable and clinically
interpretable picture. The findings emphasise the importance of incorporating the assessment and treatment of relevant
symptoms, functions and social support in people recently diagnosed with advanced cancer as part of their clinical care.
Cancer will develop in 45% of men and 40% of women during their
lifetime, and about 1 in 4 will die of the disease (Marrett et al,
2008). The survival rates for most tumours are, however,
continually improving owing to earlier detection, continued
improvement in treatment therapies and better general medical
management (Marrett et al, 2008). Owing to its improved survival,
cancer is now considered a chronic disease (Canadian Academy of
Health Sciences, 2010), and as a result, concerns about the
well-being and the quality of life (QOL) of people with cancer
has become paramount in clinical research (Food and Drug
Administration, 2006). Health-care professionals are also becom-
ing increasingly exposed to the benefits of assessing QOL in
daily clinical practice. But the understanding of the scientific
basis underlying QOL assessment still needs to be established
(Osoba, 2007).
A comprehensive model of health-related QOL (HRQL) was
developed by Wilson and Cleary (1995). This conceptual model
suggests causal links among biological and physiological factors,
symptoms, functional levels, general health perceptions and overall
QOL. Individual and environmental characteristics also influence
each of the components of the model (Wilson and Cleary, 1995).
The Wilson and Cleary Model of HRQL for people with cancer can
be seen in Figure 1.
The Wilson and Cleary model was partly assessed for patients
with gastrointestinal bleeding (Sousa and Williamson, 2003),
Parkinson’s disease (Straits-Troster et al, 2000; Chrischilles et al,
2002), heart disease (Bennett et al, 2001; Heo et al, 2005; Lee et al,
2005a; Mathisen et al, 2007), HIV/AIDS (Wilson and Cleary, 1996,
1997; Sousa et al, 1999; Cosby et al, 2000; Hays et al, 2000; Sousa
and Chen, 2002; Cunningham et al, 2005; Sousa and Kwok, 2006),
*Correspondence: Dr AM Rodrı
´guez; E-mail: ana.rodriguez@mail.mcgill.ca
Received 21 November 2012; revised 2 March 2013; accepted 7 March 2013; published online 16 April 2013
&2013 Cancer Research UK. All rights reserved 0007 – 0920/13
FULL PAPER
Keywords: quality of life; regression; effect size
British Journal of Cancer (2013) 108, 1790–1800 | doi: 10.1038/bjc.2013.146
1790 www.bjcancer.com | DOI:10.1038/bjc.2013.146

renal disease (Molzahn et al, 1996). It was minimally examined in
people with cancer, as only one study examined the model with
survivors with Hodgkin’s lymphoma (Wettergren et al, 2004).
The purpose of this study was therefore to estimate, for people
with a newly diagnosed advanced cancer, the extent to which
biological and physiological factors, symptoms, function and
general health perceptions predict overall QOL, as hypothesised
by the Wilson and Cleary Model of HRQL.
MATERIAL AND METHODS
Participants. Adults were recruited if they have had a recent
diagnosis of advanced cancer and had been referred to the McGill
University Health Center or the Jewish General Hospital oncology
clinics, in Montreal, Canada. Advanced cancer was defined as
unresectable stage 3A, 3B or 4 non-small-cell lung cancer; stage 3
or 4 upper gastrointestinal cancer; stage 4 colorectal, hepatobilliary
or head and neck cancers; breast and prostate cancers with visceral
metastases; and all stages of pancreatic cancers. The inclusion
criterion included an estimated life expectancy of 3 months or
more and an Eastern Cooperative Oncology Group Performance
Status Score of 0 to 3 to represent varying degrees of disabilities but
sufficient function to complete the assessments (Oken et al, 1982).
Patients were not eligible if they were unable to comply with study
instructors or if they had symptomatic brain metastases.
Procedure. The study was approved by the hospitals’ Institutional
Review Board. The eligibility of patients was verified by a member
of their primary oncology team who also obtained verbal consent
to be approached by study personnel. If patients consented to
participate, an appointment was made for the assessment. At their
first assessment, participants were assessed using patient-reported
outcomes and direct measures representing the domains of the
Wilson and Cleary conceptual model of HRQL. If patients refused
to participate, sociodemographic information such as their age,
their primary tumour origin and their gender were collected, as
were their self-perceived health from 0 to 10. This was done to
estimate whether there was a sampling bias between participants
and non-participants.
Measurement. The measurement strategy included characterising
the sample and selecting relevant items and domains from widely
used health outcomes measures in cancer.
The outcome of interest in this study was the construct of
overall QOL. One subscale and two single items were used to
represent overall QOL: the existential domain of the McGill
Quality Of Life Questionnaire (MQOL-existential), the single-item
scale of the McGill Quality of Life Questionnaire (MQOL-SIS) and
the QOL item of the Edmonton Symptom Assessment System (first
version) (ESAS-QOL).
Fifty-seven explanatory variables and eight potential confounder
variables were included in the analyses. The variables were chosen
to represent the different domains of the Wilson and Cleary model.
The measures used and their psychometric properties are fully
described in the Appendices A and B (Table A1 and A2),
recognising that the individual items and subscales were the
elements used in the analyses.
Biological and physiological indicators were also collected.
These included C-reactive protein serum concentration levels,
recent recalled weight loss at the time of diagnosis, the body mass
index, the skeletal muscle index and the presence of sarcopenia.
In addition, personal factors such as age, sex, the site of the
original tumour, the number of comorbidities, the highest level of
education completed and the country of birth were recorded on the
day of testing. Social environmental characteristics were also
collected, such as the marital status and the number of children.
Statistical methods. Three different subscale/items (MQOL-
existential, MQOL-SIS and ESAS-QOL) represented the outcome
construct, overall QOL. Consequently, three independent analyses
were performed to determine the most important contributors of
overall QOL.
Descriptive statistics were used to characterise the participants
and the distribution of variables. Mean values and standard
deviations for continuous variables, as well as frequencies and
percentages for categorical variables, summarised patients’ char-
acteristics. Age, sex, primary tumour site, years of education,
cultural origin, number of comorbidities, number of children and
marital status were examined for their potential for confounding.
Univariate linear regressions were used to screen the associa-
tions between the 57 potential contributors to each representation
Sex Education Cultural
background
Number of
comorbidities
Age
Characteristics of the individual
Social support Marital status
Characteristics of the social environment
Energy
Pain
Psychological
well-being
Psychological
function
Roles
participation
Social function
Cognitive
function
Physical
function
General health
perceptions
Overall
quality of life
Number
of children
Gastro-intestinal
symptoms
C-reactive
protein
Body mass
index
Skeletal muscle
Index
Recalled
weight loss
Sarcopenia
Figure 1. The Wilson and Cleary model of health-related quality of life in people with advanced cancer.
Quality of life in advanced cancer BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2013.146 1791

of overall QOL. Variables that were associated with one of the QOL
measures at P-value below or equal the 0.1 level were retained for
the further analyses.
Bivariate correlations between the retained variables and the
outcomes were examined using Pearson, polychoric and polyserial
correlation coefficients. All assumptions of linear regression were
examined, and there were no serious violations.
Three independent forward stepwise multiple linear regressions
were performed to predict overall QOL. The 10 variables
explaining the most variability per outcome were ranked by partial
R
2
order. Effect sizes were also estimated for these variables using
t-values (Cohen, 1988; Liang et al, 1990), which is a quantitative
similar to Cohen’s d. In the context of linear regression, the t-value
corresponds to the difference in least-squares estimators divided by
the standard error of the least-squares estimators. In an attempt to
identify constructs with more consistent associations with overall
QOL, the partial R
2
rankings and effect sizes of the identified
contributing variables were averaged per construct and across all
three outcomes of overall QOL.
Stepwise multiple linear regression is an analytical approach
that has the capacity to select a statistical model ‘when there is a
large number of potential explanatory variables and no underlying
theory on which to base the model selection’ (Pace, 2008). This
automatism of the procedure has been previously described as its
limitation. However, in this study, the Wilson and Cleary
theoretical model of HQOL directed the selection of the variables
included in the analyses. Also, this statistical approach has the
advantage of preventing bias imposed by the investigators upon the
selection of final model.
All statistical analyses were carried out using the Statistical
Analysis Systems version 9.2.
RESULTS
Description of the sample. Three thousand seven hundred fifty
one patients were screened for eligibility. Of the 388 eligible
patients, 203 patients (52.3%) consented to participate and
completed the initial evaluation (Figure 2).
The average age was 63 years (±13) and 59.3% of participants
were men. The most common primary tumour origins were the
pancreas (22.6%), followed by lung (16.7%), and the colorectal
tract (12.3%). Patient characteristics are presented in Table 1.
Sociodemographic information was collected from 157 patients
who refused to participate. The age and gender distribution was
similar in both the participants and the non-participants: the
average age was of 67 years (±11.6) and 56.1% were men.
The most common primary tumours sites in these patients were
the lung (18.5%), followed by the pancreas (14.7%) and head or
neck (14.0%). Participants and non-participants were similar (P-
value ¼0.80) on perceived health rated on a scale of 0 to 10: 6.8
(±2.1) and 6.0 (±2.1), respectively.
The distributions of the three outcomes of overall QOL are
presented in Figure 3. MQOL-Existential, MQOL-SIS and ESAS-
QOL (rescored) all ranged from 0–10, 10 indicating best quality of
life and 0 the worst. Participants rated their QOL similarly with all
measures of overall QOL. The medians were 7.9 for MQOL-
Existential and 7.0 for MQOL-SIS and ESAS-QOL. The inter-
quartile ranges spanned 2 units for MQOL-Existential, 3 for
MQOL-SIS and 4 for ESAS-QOL. Of the three outcomes of overall
QOL, MQOL-Existential had a smaller distribution, not unex-
pected with a multi-item index.
Univariate associations. The screening of the associations
between the 57 potential explanatory variables and each measure
of overall QOL by simple linear regression led to the elimination of
between 7 and 17 variables per outcome variable. Of the retained
Patients assessed for eligibility
n=3751
Not eligible
n=3363
Patient approached and informed about the study
n=388
Refused
n=153
Consented
n=235
Did not complete
assessment
n=32
Analysed
n=203
Figure 2. Flowchart.
Table 1. Demographic and clinical characteristics of study participants
Characteristic Participants
(n¼203), (%)
Age (years) categories (mean: 63.3, s.d. 12.9)
o35 7 (3.3)
36–50 23 (11.3)
51–64 72 (35.3)
X65 101 (49.8)
Sex
#/~120/83
% 59.9/40.9
Primary tumour site
Pancreatic 46 (22.6)
Lung 34 (16.7)
Colorectal 25 (12.3)
Upper GI 23 (11.3)
ENT 23 (11.3)
Breast 20 (9.8)
Hepatobilliary 17 (8.3)
Prostate 7 (3.4)
Urological 3 (1.5)
Unconfirmed primary origin 2 (1.0)
Ovarian 1 (0.5)
Retroperitoneal 1 (0.5)
Skin—basal cell 1 (0.5)
Number of comorbidities (mean 2.4, s.d. 1.5)
0 (Cancer only) 74 (36.5)
151(25.1)
X278(38.4)
Abbreviations: GI ¼gastrointestinal; s.d.¼standard deviation
BRITISH JOURNAL OF CANCER Quality of life in advanced cancer
1792 www.bjcancer.com | DOI:10.1038/bjc.2013.146

variables, 34 had statistically significant associations with all three
outcomes of overall QOL, 7 with two outcomes and only 3 were
associated with only one outcome of overall QOL.
The univariate correlation coefficients were consistent with
expectations. The correlation coefficients between the contributor
variables and the outcomes varied from 0.01 to 0.59.
Stepwise multiple linear regression analyses. Table 2 summarises
the rankings in partial R
2
, from 1 (the most important) to 10 (the
least important), of the first 10 variables identified by the
three independent stepwise regressions, using the ranking for
MQOL-Existential for the ordering of variables. Also presented are
the effect sizes as measured by the t-test value.
When overall QOL was represented by MQOL-Existential, the
variable with the highest partial R
2
was general health perception
(GHP) from the RAND-36, followed by the psychological domain
of the MQOL (rank 2) and social support domain (rank 3). When
overall QOL was represented by MQOL-SIS, GHP (from MQOL)
retained the first rank, followed by the psychological domain of
the MQOL (rank 2), and an item of the Faact measuring appetite
(rank 3).
When overall QOL was represented by ESAS-QOL, a subscale
measuring physical function in the MFI held the first rank; an item
measuring fatigue from the ESAS was ranked second, followed by
the social support domain of the MQOL (rank 3).
The last two columns of Table 2 present the averages of the R
2
rankings and of the effect sizes. Using R
2
, the most important
constructs contributing to overall QOL was social support and
GHP (both with an average R
2
ranking of 3.0), followed by
psychological distress, and relatively closely by fatigue. Using effect
sizes, the same general order of the most important contributors
remained. However, the effect sizes identified social function as
much an important contributor. Both R
2
and effect sizes ranked
physical function and symptoms profile in the same order. The two
methods of average ranking produced statistically similar
hierarchies when compared using the Wilcoxon signed-rank
test (P¼0.58).
We represented the hierarchy of contributors of the overall QOL
in people with advanced cancer as a pyramid analogous to
Maslow’s Hierarchy of Needs (Maslow, 1948) (Figure 4). As the
pyramid identified needs, some constructs were modified to convey
a positive meaning (‘fatigue’ was for instance modified to ‘energy’).
Overall QOL
10
7
2
0
Distribution of overall QOL outcomes
MQOL -
existential
MQOL-SIS ESAS-QOL
*: Horizontal line represents median, x represents average mean
Figure 3. Distribution of overall QOL outcomes.
Table 2. Relative ranking and effect sizes of items measuring symptoms, function and general health perception for QOL using adjusted R
2
-stepwise
regression
QOL outcomes
MQOL-existential
(total R
2
¼0.66)
MQOL-SIS
(total R
2
¼0.69)
ESAS-QOL
(total R
2
¼0.68)
Partial R
2
rank
Effect size
(t-value)
Partial R
2
rank
Effect size
(t-value)
Partial R
2
rank
Effect size
(t-value)
Average
R
2
ranks
a
Average effect
sizes
a
(t-value)
Contributor constructs
General health
perception
1(R
2
¼0.32) 4.9 1, 9
b
(R
2
¼0.50)
8.6, 3.5
b
3.0 5.5
Psychological distress 2 6.5 2 3.3 7 2.3 3.7 4.0
Social support 3 7.1 3 4.0 3.0 5.6
Gastrointestinal symptoms
Smell 4 4.8 6.7 3.0
Lack of appetite 5 2.8 3, 7, 10
b
3.9,2.9, 2.0
b
10 2.5
Taste 9 3.6 6 2.1
Vomiting 42.6
Stomach pain 82.0
Interest in food 92.2
Fatigue 6, 7
b
3.8, 4.3
b
4 3.2 2 5.2 4.2 4.2
Social function 8 4.0 8.0 4.0
Pain 6 2.3 5 2.2 5.5 2.3
Physical function 10 4.2 5, 8
b
3.5, 4.2
b
1(R
2
¼0.39) 3.1 5.8 3.7
a
Average R
2
rank: lower is first; Average effect sizes: higher is first.
b
By individual item contribution. Also represented is the Partial R
2
of the 1st ranked item.
Quality of life in advanced cancer BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2013.146 1793

DISCUSSION
Using multiple stepwise linear regression models, a large number
of potential contributors to overall QOL were reduced to a
manageable and interpretable clinical picture. Similar and some-
times different contributors were identified according to how the
latent construct of overall QOL was represented.
Apart from random error, differences in the importance rankings
by outcome undoubtedly arise from differences in the QOL
outcomes themselves. Two outcomes were single items (MQOL-
SIS and ESAS-QOL) and one was a subscale with a total score
derived from averaging scores on 6 items (MQOL-Existential).
In the MQOL-SIS, the patient is asked to contemplate all aspects
of his or her life (physical, emotional, social, spiritual and financial)
(Cohen et al, 1997) and provide a value between 0 and 10. MQOL-
Existential includes concerns regarding death, freedom, isolation
and the meaning of life, as existential concerns have been
demonstrated to be of great importance to people with a life-
threatening illness and is under-represented in many measures
assessing QOL (O’Connor et al, 1990; Fryback, 1993).
In contrast, ESAS-QOL is 1 of 10 visual analogue scales (VAS)
describing how a person would best describe their health in the last
24 h. The health states include QOL and a variety of physical
symptoms, usually of negative connotation such as fatigue, nausea,
depression or pain (Bruera et al, 1991). It is therefore possible that
although patients are asked to rate their general QOL, the context
in which the item is asked is likely to influence the rating. Our
study found that the contributing variables to the ESAS-QOL
single-item were almost inversely ordered in terms of importance
with respect to MQOL-Existential and MQOL-SIS.
We translated the findings from combining the results from the
different the regression analyses into a ‘pyramid of needs’
mimicking Maslow’s Hierarchy of Needs (Maslow, 1948) to
emphasise that these are key areas of everyday life and function
that people with threatened health need in order to continue to
view life as worth living (World Health Organisation, 2001).
Social support was found to be the most important contributor to
overall QOL meaning that, on average, people with advanced cancer who
that reported being supported by their social surrounding also reported
higher levels of overall QOL. By the term ‘social support’, we refer mainly
to the resources provided by other persons (Cohen and Syme, 1985). It
has also been defined as the cognitive appraisal of being ‘reliably
connected to significant others in a given social environment (Mathisen
et al, 2007). Interestingly, it also fits with another identified contributor to
overall QOL, social function. Social function can be defined as the actions
and tasks required for basic and complex interactions with people in a
contextually and socially appropriate manner (Cao et al,2012).Therefore,
the two concepts are closely related, as a socially functional individual will
likely have a strong social support system in place that could be used as a
coping mechanism and vice-versa.
Social support is becoming recognised as an important
contributor of overall QOL in people with cancer. In a study on
the prevalence and contributors to the unmet needs and their
association with QOL, 296 men with advanced cancer were
evaluated. Social support scores significantly predicted total overall
QOL scores, to the same extent that psychological or physical
symptoms did (Hwang et al, 2004). Recently, several studies have
been recommending measuring social support as part of the
assessment of people with cancer as they are key elements of their
well-being (Gallagher and Vella-Brodrick, 2008; Hahn et al, 2010;
McCabe and Cronin, 2011). The role of social support at end-of-
life is recognised and is one of the key roles played by volunteers in
hospice system (Pesut et al, 2012).
Other important contributors to overall QOL were fatigue,
psychological distress, pain and physical function. This is
consistent with the published literature. Pain, depression and
fatigue are highly prevalent in cancer patients, and they often
coexist. Laird et al (2011) recently identified that pain, depression
and fatigue was an identifiable symptom cluster in a cohort of
advanced cancer patients and is associated with reduced physical
functioning. Similarly, a study on 1630 stage 3 and 4 Danish cancer
patients identified the most prevalent symptoms contributing to a
deterioration of QOL (Johnsen et al, 2009) as being fatigue (57%;
severe 22%) followed by reduced role function, insomnia and pain
(Johnsen et al, 2009). The importance of the prevalence of these
symptoms is such that in 2003, the National Institute of Health
convened a State-of-the-Science Conference on pain, depression
and fatigue symptom management in people with cancer in order
to identify directions for future research (Patrick et al, 2003).
The self-reported overall QOL found in our study was strikingly
similar to other studies with comparable populations. Lowe et al
(2009) evaluated 50 adult advanced cancer patients with estimated
life expectancies of 3 to 12 months from outpatient palliative care
clinic and home care. Patients obtained a mean QOL score of
7.4±1.4 on the MQOL-Existential and 6.1±2.0 on ESAS-QOL
(scale reversed from the original score of 3.9±2.0) (Lowe et al,
2009). Similarly, 38 patients with advanced cancer were evaluated
using MQOL-Existential and obtained a mean score of 7.9±1.2
(Sherman et al, 2006). The same can be observed for reports of the
MQOL-SIS: Jones et al (2010) obtained a mean MQOL-SIS score of
6.1±1.4 when assessing 211 cancer patients admitted to an acute
palliative care unit in a comprehensive cancer center.
This study included only patients with advanced disease, so the
results may not be generalise to patients at the early stages of the
disease. Regression approaches identify only those variables that, in
the presence of all others, make a unique and direct contribution to
the outcome, here overall QOL. A limitation is that variables which
impact indirectly through other variables are not identified;
nevertheless this approach provides a minimum portfolio of
variables, which would be a starting in developing a more complex
model requiring structural equation modelling (SEM). Another
limitation of this approach is that the latent construct of overall
QOL had to be modelled as different variables; SEM would allow
the different representations of overall QOL to contribute
statistically to a latent variable.
We demonstrated a novel way of using multivariate linear
regressions to make sense of a large amount of information to a
more manageable and clinically interpretable picture. However, the
variation in the contributors to QOL has relevant implications for
the clinical management of patients with advanced cancer.
Depending on the instrument used, the focus of the interventions
by the various health professionals would be different. Also, in the
research setting, the choice of the instrument will greatly influence
the ‘measured’ change in QOL secondary to the intervention(s)
under study.
Social support
General health perception
Energy
Social function
Psychological health
Physical function
Absence of gastrointestinal symptoms
Absence of pain
Figure 4. The hierarchy of contributors to QOL in people with
advanced cancer.
BRITISH JOURNAL OF CANCER Quality of life in advanced cancer
1794 www.bjcancer.com | DOI:10.1038/bjc.2013.146

Social support is identified as the most important contributor to
overall QOL in people with a recent diagnosis of advanced cancer.
For health-care practitioners, this translates into assessing or
asking patients recently diagnosed with cancer about their social
networks and support and to arrange access for support when it is
absent. The results also suggest paying particular attention to
assessing and controlling physical function, fatigue, psychological
distress, pain and gastrointestinal symptoms from the time of
diagnosis. This would indicate that a team approach to measure-
ment and care through the involvement of health-care
professionals whose expertise lie in these domains (physical
and occupational therapists, psychologists, social workers,
nutritionists and palliative care physicians) would complement
usual oncology care.
An interdisciplinary team approach, with a particular focus on
physical function and fatigue, was found to be associated with
improvement in overall QOL for patients with head and neck
cancer (Eades et al, 2013). A recent clinical trial on the effect of
early involvement of palliative care physicians and nurses in the
care of patients with advanced lung cancer reported a significant
improvement in overall QOL for the intervention group compared
with patients receiving usual care (Temel et al, 2010). As the
outcome for this study included items measuring physical function
and fatigue, the effect may have been larger if the team had
included health-care professionals with specialized expertise in
those domains.
The involvement of health-care professionals with specific
expertise in the management of cancer-related symptoms,
psychological distress and loss of physical and social functions,
supported by the integrated involvement of volunteers (Pesut et al,
2012), should be considered the new standard of care for patients
with advanced cancer with decreased overall QOL. This is
particularly important as fewer than 10% of oncology patients
have been reported to receive psychosocial therapy (Lee et al,
2005b).
Modern health-care emphasises patient-centered care defined
by a focus on outcomes that people notice and care about
including, not only survival, but also function, symptoms and
modifiable aspects of QOL (Patient-Centered Outcomes Research
Insitute, 2013). Thus, the measurement and optimisation of the
contributors to QOL, such as those identified in this study, would
be necessary components of a patient-centered oncology program.
ACKNOWLEDGEMENTS
This research was supported by a grant from the Terry Fox
Research Institute. B Gagnon is a recipient of ‘Chercheur-clinicien
Boursier’ award from Fonds de recherche Sante
´Que
´bec. We also
thank Dr Neil MacDonald, Dr Lorenzo Ferri, Dr Peter Metrakos,
Dr Linda O’Faria, Dr Catalin Mihalcioiu, Dr Victor Cohen, Dr
Carmela Pepe, Dr David Small, Dr Chaudhury and Dr Prosanto,
for their input or help in recruiting participants. We finally with to
thank the participants and their families who gave their valuable
time to participate in the study.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
REFERENCES
Bennett SJ, Perkins SM, Lane KA, Deer M, Brater DC, Murray MD (2001)
Social support and health-related quality of life in chronic heart failure
patients. Qual Life Res 10(8): 671–682.
Bruera E, Kuehn N, Miller MJ, Selmser P, Macmillan K (1991) The Edmonton
Symptom Assessment System (ESAS): a simple method for the assessment
of palliative care patients. J Palliat Care 7(2): 6–9.
Canadian Academy of Health Sciences (2010) Transforming Care for
Canadians with Chronic Health Conditions: Put People First, Expect the
Best, Manage for Results, Ottawa.
Cao MD, Sitter B, Bathen TF, Bofin A, Lonning PE, Lundgren S, Gribbestad IS
(2012) Predicting long-term survival and treatment response in breast
cancer patients receiving neoadjuvant chemotherapy by MR metabolic
profiling. NMR Biomed 25(2): 369–378.
Chrischilles EA, Rubenstein LM, Voelker MD, Wallace RB, Rodnitzky RL
(2002) Linking clinical variables to health-related quality of life in
Parkinson’s disease. Parkinsonism Relat Disord 8(3): 199–209.
Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences.
Academic Press: New York, NY, USA.
Cohen S, Syme SL (1985) Issues in the study and application of social support.
In Cohen S, Syme SL (eds) Social Support and Health. pp 3–41, Academic
Press Inc.: Orlando, FL, USA.
Cohen SR, Mount BM, Bruera E, Provost M, Rowe J, Tong K (1997) Validity
of the McGill quality of life questionnaire in the palliative care setting: a
multi-centre Canadian study demonstrating the importance of the
existential domain. Palliative Med 11(1): 3–20.
Cosby C, Holzemer WL, Henry SB, Portillo CJ (2000) Hematological
complications and quality of life in hospitalized AIDS patients. AIDS
Patient Care STDS 14(5): 269–279.
Cunningham WE, Crystal S, Bozzette S, Hays RD (2005) The association of
health-related quality of life with survival among persons with HIV
infection in the United States. J Gen Intern Med 20(1): 21–27.
Eades M, Murphy J, Carney S, Amdouni S, Lemoignan J, Jelowicki M, Nadler
M, Chasen M, Gagnon B (2013) Effect of an interdisciplinary
rehabilitation program on quality of life in patients with head and neck
cancer: review of clinical experience. Head Neck 35(3): 343–349.
Food and Drug Administration (2006) Guidance for industry: patient-
reported outcome measures: use in medical product development to
support labeling claims: draft guidance. Health Qual Life Outcomes
4: 79.
Fryback PB (1993) Health for people with a terminal diagnosis. Nurs Sci Q
6(3): 147–149.
Gallagher EN, Vella-Brodrick DA (2008) Social support and emotional
intelligence as predictors of subjective well-being. Person Individual
Differences 44(7): 1551–1561.
Hahn EA, Devellis RF, Bode RK, Garcia SF, Castel LD, Eisen SV, Bosworth
HB, Heinemann AW, Rothrock N, Cella D (2010) Measuring social health
in the patient-reported outcomes measurement information system
(PROMIS): item bank development and testing. Qual Life Res 19(7):
1035–1044.
Hays RD, Cunningham WE, Sherbourne CD, Wilson IB, Wu AW,
Cleary PD, McCaffrey DF, Fleishman JA, Crystal S, Collins R, Eggan F,
Shapiro MF, Bozzette SA (2000) Health-related quality of life in patients
with human immunodeficiency virus infection in the United States:
results from the HIV Cost and Services Utilization Study. Am J Med
108(9): 714–722.
Heo S, Moser DK, Riegel B, Hall LA, Christman N (2005) Testing a published
model of health-related quality of life in heart failure. J Card Fail 11(5):
372–379[pii].
Hwang SS, Chang VT, Cogswell J, Alejandro Y, Osenenko P, Morales E,
Srinivas S, Kasimis B (2004) Study of unmet needs in symptomatic
veterans with advanced cancer: incidence, independent predictors and
unmet needs outcome model. J Pain Symptom Manage 28(5): 421–432.
Johnsen AT, Petersen MA, Pedersen L, Groenvold M (2009) Symptoms and
problems in a nationally representative sample of advanced cancer
patients. Palliat Med 23(6): 491–501.
Jones JM, Cohen SR, Zimmermann C, Rodin G (2010) Quality of life and
symptom burden in cancer patients admitted to an acute palliative care
unit. J Palliat Care 26(2): 94–102.
Laird BJ, Scott AC, Colvin LA, McKeon AL, Murray GD, Fearon KC, Fallon
MT (2011) Pain, depression, and fatigue as a symptom cluster in advanced
cancer. J Pain Symptom Manage 42(1): 1–11.
Lee DT, Yu DS, Woo J, Thompson DR (2005a) Health-related quality of life in
patients with congestive heart failure. Eur J Heart Fail 7(3): 419–422.
Lee SJ, Loberiza FR, Antin JH, Kirkpatrick T, Prokop L, Alyea EP, Cutler C,
Ho VT, Richardson PG, Schlossman RL, Fisher DC, Logan B, Soiffer RJ
(2005b) Routine screening for psychosocial distress following
Quality of life in advanced cancer BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2013.146 1795

hematopoietic stem cell transplantation. Bone Marrow Transplant 35(1):
77–83.
Liang MH, Fossel AH, Larson MG (1990) Comparisons of five health status
instruments for orthopedic evaluation. Med Care 28(7): 632–642.
Lowe SS, Watanabe SM, Baracos VE, Courneya KS (2009) Associations
between physical activity and quality of life in cancer patients receiving
palliative care: a pilot survey. J Pain Symptom Manage 38(5): 785–796.
Marrett LD, De P, Airia P, Dryer D (2008) Cancer in Canada in 2008. CMAJ
179(11): 1163–1170.
Maslow AH (1948) Higher and lower needs. J Psychol 25: 433–436.
Mathisen L, Andersen MH, Veenstra M, Wahl AK, Hanestad BR, Fosse E
(2007) Quality of life can both influence and be an outcome of general
health perceptions after heart surgery. Health Qual Life Outcomes 5: 27.
McCabe C, Cronin P (2011) Issues for researchers to consider when using
health-related quality of life outcomes in cancer research. Eur J Cancer
Care 20(5): 563–569.
Molzahn AE, Northcott HC, Hayduk L (1996) Quality of life of patients with
end stage renal disease: a structural equation model. Qual Life Res 5(4):
426–432.
O’Connor AP, Wicker CA, Germino BB (1990) Understanding the cancer
patient’s search for meaning. Cancer Nurs 13(3): 167–175.
Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET,
Carbone PP (1982) Toxicity and response criteria of the Eastern
Cooperative Oncology Group. Am J Clin Oncol (6): 649–655.
Osoba D (2007) Translating the science of patient-reported outcomes
assessment into clinical practice. J Natl Cancer Inst Monogr (37): 5–11.
Pace NL (2008) Independent predictors from stepwise logistic regression may be
nothing more than publishable P values. Anesth Analg 107(6): 1775–1778.
Patient-Centered Outcomes Research Insitute (2013) http://www.pcori.org/
about/mission-and-vision/.
Patrick DL, Ferketich SL, Frame PS, Harris JJ, Hendricks CB, Levin B, Link
MP, Lustig C, McLaughlin J, Ried LD, Turrisi III AT, Unutzer J, Vernon
SW (2003) National institutes of health state-of-the-science conference
statement: symptom management in cancer: pain, depression, and fatigue,
15–17 July, 2002. J Natl Cancer Inst 95(15): 1110–1117.
Pesut B, Hooper B, Lehbauer S, Dalhuisen M (2012) Promoting volunteer
capacity in hospice palliative care: a narrative review. Am J Hospice
Palliative Med; e-pub ahead of print 31 December 2012.
Sherman DW, Ye XY, McSherry C, Parkas V, Calabrese M, Gatto M (2006)
Quality of life of patients with advanced cancer and acquired immune
deficiency syndrome and their family caregivers. J Palliat Med 9(4):
948–963.
Sousa KH, Chen FF (2002) A theoretical approach to measuring quality of life.
J Nurs Meas 10(1): 47–58.
Sousa KH, Holzemer WL, Henry SB, Slaughter R (1999) Dimensions of
health-related quality of life in persons living with HIV disease. J Adv Nurs
29(1): 178–187.
Sousa KH, Kwok OM (2006) Putting Wilson and Cleary to the test: analysis of
a HRQOL conceptual model using structural equation modeling. Qual Life
Res 15(4): 725–737.
Sousa KH, Williamson A (2003) Symptom status and health-related quality of
life: clinical relevance. J Adv Nurs 42(6): 571–577.
Straits-Troster K, Fields JA, Wilkinson SB, Pahwa R, Lyons KE, Koller WC,
Troster AI (2000) Health-related quality of life in Parkinson’s disease after
pallidotomy and deep brain stimulation. Brain Cogn 42(3): 399–416.
Temel JS, Greer JA, Muzikansky A, Gallagher ER, Admane S, Jackson VA,
Dahlin CM, Blinderman CD, Jacobsen J, Pirl WF, Billings JA, Lynch TJ
(2010) Early palliative care for patients with metastatic non-small-cell lung
cancer. N Engl J Med 363(8): 733–742.
Wettergren L, Bjorkholm M, Axdorph U, Langius-Eklof A (2004)
Determinants of health-related quality of life in long-term survivors of
Hodgkin’s lymphoma. Qual Life Res 13(8): 1369–1379.
Wilson IB, Cleary PD (1995) Linking clinical variables with health-related
quality of life. A conceptual model of patient outcomes. JAMA 273(1):
59–65.
Wilson IB, Cleary PD (1996) Clinical predictors of functioning in persons
with acquired immunodeficiency syndrome. Med Care 34(6): 610–623.
Wilson IB, Cleary PD (1997) Clinical predictors of declines in physical
functioning in persons with AIDS: results of a longitudinal study. J Acquir
Immune Defic Syndr Hum Retrovirol 16(5): 343–349.
World Health Organization (2001) International Classification of Functioning,
Disability, and Health: ICF. World Health Organization: Geneva,
Switzerland.
This work is published under the standard license to publish agree-
ment. After 12 months the work will become freely available and
the license terms will switch to a Creative Commons Attribution-
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APPENDIX A
Table A1. Description and Psychometric Properties of the Measures
Measure Description of measure Psychometric
properties
McGill QOL Ques-
tionnaire (MQOL)
The McGill Quality of
Life Questionnaire
(MQOL) was designed
to measure QOL at all
stages of a life-threaten-
ing illness, from diag-
nosis to cure or death
(Cohen et al, 1995,
1996b, 1997, 2001;
Cohen and Mount,
2000). It comprises 16
self-report items that
are rated on a scale of
0 (the worst) to 10 (the
best) and based on a
two-day time frame.
Five domains (physical
symptoms, psychologi-
cal symptoms, existen-
tial well-being, physical
well-being and support)
are computed from the
score or the mean scores
of 1 to 6 items. In
addition, the MQOL
Good levels of reliabil-
ity and validity in peo-
ple with cancer
(Cohen et al, 1995;
1996b, 1997, 2001;
Cohen and Mount,
2000). Construct valid-
ity and internal con-
sistency reliability of
the domains was
demonstrated in other
palliative populations
as well (Cohen et al,
1996a, 1997).
Table A1. ( Continued )
Measure Description of measure Psychometric
properties
includes a single-item
scale (MQOL-SIS), also
scored from 0 to 10, and
constructed to measure
overall QOL.
Edmonton symptom
assessment system
(ESAS)
The ESAS is a 10-item
symptom visual analo-
gue scale (VAS) devel-
oped for use in
symptom assessment of
palliative care patients
(Richardson and Jones,
2009). The patients rate
the severity of the fol-
lowing nine symptoms:
pain, fatigue, nausea,
depression, anxiety,
drowsiness, lack of
appetite, itching and
shortness of breath on
a 10-cm line. The sever-
ity for each symptom is
rated from 0 to 10, 0
being an absent symp-
tom and 10 being of the
worst possible severity.
There is an additional
An acceptable level of
validity and reliability
of the measure has
been reported (Chang
et al, 2000; Nekolai-
chuk et al, 2008;
Richardson and Jones,
2009).
BRITISH JOURNAL OF CANCER Quality of life in advanced cancer
1796 www.bjcancer.com | DOI:10.1038/bjc.2013.146

Table A1. ( Continued )
Measure Description of measure Psychometric
properties
VAS assessing quality of
life.
Preference-based can-
cer index (PBCI)
The Preference-based
cancer index is an adap-
tation from the prefer-
ence-based stroke index,
a collection of items
intended to supplement
the EQ-5D index (Pois-
sant et al, 2003). It
includes 10 items with
a three-point Likert-
type response scale
assessing walking,
climbing stairs, physical
activities/sports, recrea-
tional activities, work,
driving, speech, mem-
ory, coping and self-
esteem. A cumulative
score can be obtained
from these preference
weights (Poissant et al,
2003).
Content validity and
construct validity of
the measure has been
demonstrated (Pois-
sant et al, 2003).
Functional assessment
of anorexia/cachexia
therapy (FAACT)
The FAACT consists of
27 Likert-type items of
the symptoms asso-
ciated with cancer and
its treatments, scored
from 0 to 4 anchored
with ‘not at all’ to ‘very
much’, with total quality
of life score ranging
from 0 to 108. The
FAACT includes the
FACT-G, with an addi-
tional 12 items of ‘addi-
tional concerns’ that
refer to problems
related to cachexia or
anorexia (Ribaudo et al,
2000). In our assess-
ment, we only included
the 12 items relating to
the cachexia/anorexia
symptoms.
Reliability and validity
of the FACT and the
FAACT measurement
system have been
recognised (Ribaudo
et al, 2000).
RAND short form 36-
item health survey
(RAND-36)—version
1
The RAND-36 is a gen-
eric health-related qual-
ity of life measure that
assesses 8 health con-
cepts: physical and
social function, usual
roles activities, pain,
vitality, mental health,
and perception of health
in general. Each item is
scored on a dichoto-
mous, three or five-
point categorical scale;
subscale scores range
from 0 to 100. Physical
and mental summary
scores can also be con-
structed (Hays et al,
1993).
Reliability, validity and
responsiveness have
been largely demon-
strated in patients with
a variety of acute and
chronic conditions
(Hays et al, 1993;
Wood-Dauphinee
et al, 1998).
EuroQol-5D (EQ-5D) The EQ-5D comprises
two sections, the EQ-
5Dindex and the EQ-
5DVAS. The EQ-5Din-
dex is a 5-item standar-
dized generic measure
It has been widely used
in studies of people
with cancer (Norum,
1996) and it yields
comparable results to
other well-known
Table A1. ( Continued )
Measure Description of measure Psychometric
properties
of HRQL measuring
mobility, self-care, usual
activities, pain/discom-
fort and anxiety/depres-
sion with a three-point
response scale. The EQ-
5DVAS is a 0–100 ther-
mometer scale that
assesses self-perceived
health status.
measures (de Haan
et al, 1993; Goodyear
and Fraumeni, 1996;
Norum, 1996).
Taste and smell indi-
cators (TSI)
The taste and smell
indicators (TSI) consist
of two single-item indi-
cators asking for distur-
bances in smell and in
taste, with a three-point
Likert-type response
scales associated with
the anchors ‘no distur-
bances’, ‘moderate dis-
turbances’ and ‘severe
disturbances or cannot
smell/taste at all’.
It yields comparable
results to other well-
known measures (de
Haan et al, 1993;
Goodyear and Frau-
meni, 1996; Norum,
1996).
Word and digit recall
questions (WDR)
To assess visual mem-
ory, we derived the
word recall question
from the delayed word
recall test, a test origin-
ally developed to facil-
itate the early diagnosis
of Alzheimer’s disease
(O’Carroll et al, 1997).
The digit sequence
learning test is a test of
attention, short-term
memory, and associative
learning (Benton et al,
1983). Subjects are
asked to repeat a string
of digits immediately
after hearing them, first
in direct and then in
reverse order. The total
number of correctly
repeated digit string
sequences was tallied.
Multidimensional fati-
gue inventory (MFI)
The Multidimensional
fatigue inventory (MFI)
is a 20-item self-report
measure of fatigue with
five dimensions: general
fatigue, physical fatigue,
mental fatigue, reduced
motivation and reduced
activity, and 4 items per
dimension, each scored
from 1 to 5. The total
score ranges from 4 to
20, a higher score indi-
cating more fatigue.
The measure was eval-
uated with cancer
patients receiving
radiotherapy and was
found to have good
internal consistency,
construct validity and
convergent validity
(Smets et al, 1995;
Schneider, 1998; Meek
et al, 2000; Fillion et al,
2003).
Modified ‘community
healthy activities
model program for
seniors physical activ-
ity measure’ (modified
CHAMPS)
The CHAMPS is a self-
report measure of phy-
sical activity, compris-
ing 40 activities
evaluated according to
the total number of
hours of activity done
in the past week. We
used a modified version
of the CHAMPS result-
ing in the physical
The measure has been
shown to be reliable,
valid and responsive in
the elderly in the com-
munity (Stewart et al,
2001a, 2001b).
Quality of life in advanced cancer BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2013.146 1797

Table A1. ( Continued )
Measure Description of measure Psychometric
properties
activities done in the
past week in total hours.
The numbers of hours
and the type of category
was then transformed
into a respective mean
metabolic equivalent
(MET) intensity level
(Ainsworth et al, 2000).
Six minute walk test
(6MWT)
The 6 min walk test
(6MWT) is a submaxi-
mal functional test of
walking endurance (Sol-
way et al, 2001). The
distance walked was
recorded both at the first
2 min and for the full
duration of the test at
6 min. The data included
here are for the test at
2 min to maximise the
data obtained, as some
fragile patients could not
complete the six minutes
of the test.
The 6MWT has been
evaluated in several
different populations
and is a valid and
reliable measure (Sol-
way et al, 2001).
Timed ‘up and go’
(TUG)
The timed up and go is
a quick and practical
test of basic mobility
skills suitable for frail
elderly persons. The
score, is the time, in
seconds, taken to stand
up from a chair, walk
3 m back-and-forth, and
sit down. Higher scores
indicate greater impair-
ment of mobility.
Concurrent validity
(Podsiadlo and
Richardson, 1991;
Venturini et al, 1995)
has been demonstrated
with correlations with
gait speed, walking
speed r¼0.71–0.96),
the Berg balance scale,
and the Barthel index
(r¼0.51).
Walking speed Gait speed is a physical
characteristic derived
from directly measuring
the parameters of dis-
tance and time. It has
been associated with
strength of the affected
lower extremity, cadence
and stride length, bal-
ance, degree of lower
extremity motor recov-
ery, and functional
mobility (Holden et al,
1986). Standardized
instructions are to walk
at a ‘comfortable’ or
‘maximum’ speed along
a walkway typically ran-
ging from 2 to 20 m
(Fransen et al,1997).In
this study, we instructed
patients to walk at a
comfortable pace speed
over a distance of 10 m,
and the time taken to
complete the middle 5 m
distance was recorded.
Gait speed is consid-
ered a valid measure of
walking ability as it
correlates with func-
tional mobility, degree
of independence in
walking, and many dif-
ferent gait parameters
(Holden et al, 1986;
Nakamura et al, 1988;
Fransen et al, 1997).
Reference List
Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM,
Strath SJ, O’Brien WL, Bassett DR, Jr, Schmitz KH, Emplaincourt
PO, Jacobs DR, Jr, Leon AS (2000) Compendium of physical
activities: an update of activity codes and MET intensities. Med Sci
Sports Exerc 32 (9 Suppl): S498–S504
Benton AL, Hamsher KS, Varney NR, Spreen O (1983) Serial
Digit Learning. In: Neuropsychological Assessment: A Clinical
Manual Oxford University Press: New York, NY, USA.
Chang VT, Hwang SS, Feuerman M (2000) Validation of the
edmonton symptom assessment scale. Cancer 88 (9): 2164–2171
Cohen SR, Boston P, Mount BM, Porterfield P (2001) Changes
in quality of life following admission to palliative care units. Palliat
Med 15 (5): 363–371
Cohen SR, Hassan SA, Lapointe BJ, Mount BM (1996a) Quality
of life in HIV disease as measured by the McGill quality of life
questionnaire. AIDS 10 (12): 1421–1427
Cohen SR, Mount BM (2000) Living with cancer: ‘good’ days
and ‘bad’ days–what produces them? Can the McGill quality of life
questionnaire distinguish between them? Cancer 89 (8): 1854–1865
Cohen SR, Mount BM, Bruera E, Provost M, Rowe J, Tong K
(1997) Validity of the McGill Quality of Life Questionnaire in the
palliative care setting: a multi-centre Canadian study demonstrat-
ing the importance of the existential domain. Palliative Med 11 (1):
3–20
Cohen SR, Mount BM, Strobel MG, Bui F (1995) The McGill
Quality of Life Questionnaire: a measure of quality of life
appropriate for people with advanced disease. A preliminary study
of validity and acceptability. Palliat Med 9(3): 207–219
Cohen SR, Mount BM, Tomas JJ, Mount LF (1996b) Existential
well-being is an important determinant of quality of life. Evidence
from the McGill Quality of Life Questionnaire. Cancer 77 (3):
576–586
de Haan R, Aaronson N, Limburg M, Hewer RL, van Crevel H
(1993) Measuring quality of life in stroke. Stroke 24 (2): 320–327
Fillion L, Gelinas C, Simard S, Savard J, Gagnon P (2003)
Validation evidence for the French Canadian adaptation of the
Multidimensional Fatigue Inventory as a measure of cancer-related
fatigue. Cancer Nurs 26 (2): 143–154
Fransen M, Crosbie J, Edmonds J (1997) Reliability of gait
measurements in people with osteoarthritis of the knee. PHYS Ther
77 (9): 944–953
Goodyear MDE, Fraumeni MA (1996) Incorporation Quality of
Life Assessment into Clinical Cancer Trials. In Spilker B (ed) pp
Quality of Life and Pharmacoeconomics in Clinical Care,
1003–1013. Lippincott-Raven Publishers: Pennsylvania, PA, USA
Hays RD, Sherbourne CD, Mazel RM (1993) The RAND-36-
Item Health Survey 1.0. Health Econ 2(3): 217–227
Holden MK, Gill KM, Magliozzi MR (1986) Gait Assessment for
Neurologically Impaired Patients: Standards for Outcome Assess-
ment. Phys Ther 66 (10): 1530–1539
Meek PM, Nail LM, Barsevick A, Schwartz AL, Stephen S,
Whitmer K, Beck SL, Jones LS, Walker BL (2000) Psychometric
testing of fatigue instruments for use with cancer patients. Nursing
Res 49 (4): 181–190
Nakamura R, Handa T, Watanabe S, Morohashi I (1988)
Walking cycle after stroke. Tohoku J Exp Med 154 (3): 241–244
Nekolaichuk C, Watanabe S, Beaumont C (2008) The Edmonton
Symptom Assessment System: a 15-year retrospective review of
validation studies (1991–2006) Palliat Med 22 (2): 111–122
Norum J (1996) Quality of life (QoL) measurement in
economical analysis in cancer: A comparison of the EuroQol
questionnaire, a simple QoL-scale and the global QoL measure of
the EORTC QLQ-C30. Oncol Rep 3787–791
O’Carroll RE, Conway S, Ryman A, Prentice N (1997)
Performance on the delayed word recall test (DWR) fails to
differentiate clearly between depression and Alzheimer’s disease in
the elderly. Psychol Med 27 (4): 967–971
Podsiadlo D, Richardson S (1991) The timed up and go—a test
of basic functional mobility for frail elderly persons. J Am Geriatr
Soc 39 (2): 142–148
BRITISH JOURNAL OF CANCER Quality of life in advanced cancer
1798 www.bjcancer.com | DOI:10.1038/bjc.2013.146

Poissant L, Mayo NE, Wood-Dauphinee S, Clarke AE (2003)
The development and preliminary validation of a preference-based
stroke index (PBSI) Health Qual Life Outcomes 1 43, doi:10.1186/
1477-7525-1-43 [doi];1477-7525-1-43 [pii]
Ribaudo JM, Cella D, Hahn EA, Lloyd SR, Tchekmedyian NS,
Von RJ, Leslie WT (2000) Re-validation and shortening of the
functional assessment of anorexia/cachexia therapy (FAACT)
questionnaire. Qual Life Res 9(10): 1137–1146
Richardson LA, Jones GW (2009) A review of the reliability and
validity of the Edmonton Symptom Assessment System. Curr
Oncol 16 (1): 55
Schneider RA (1998) Reliability and validity of the multi-
dimensional fatigue inventory (MFI-20) and the Rhoten fatigue
scale among rural cancer outpatients. Cancer Nursing 21 (5):
370-373
Smets EM, Garssen B, Bonke B, de Haes JC (1995) The
Multidimensional Fatigue Inventory (MFI) psychometric qualities
of an instrument to assess fatigue. J Psychosom Res 39 (3): 315–325
Solway S, Brooks D, Lacasse Y, Thomas S (2001) A qualitative
systematic overview of the measurement properties of functional
walk tests used in the cardiorespiratory domain. Chest 119 (1):
256–270
Stewart AL, Mills KM, King AC, Haskell WL, Gillis D, Ritter PL
(2001a) CHAMPS physical activity questionnaire for older
adults: outcomes for interventions. Med Sci Sports Exercise 33
(7): 1126–1141
Stewart AL, Verboncoeur CJ, McLellan BY, Gillis DE, Rush S,
Mills KM, King AC, Ritter P, Brown BW, Jr., Bortz WM (2001b)
Physical activity outcomes of CHAMPS II: a physical activity
promotion program for older adults. J Gerontol Ser A Biol Sci Med
Sci 56 (8): M465–M470
Venturini, A, Wilford, C, Finch, L, Spetsieris, P, and Rubinoff, S
1995. The Timed Up and Go: Reliability and Validity in an Acute
Neurological Population. World Congress of Physiotherapy
(Abstract).
Wood-Dauphinee, S, Mayo, N, Jung, H, and Shen, N. Does the
SF-36 reflect changes in the health of people with stroke? 5th
Annual Conference of the International Society for Quality of Life
Research: 15–17 November 1998, Baltimore, MD, USA. [Abstract].
Quality Life Res. 7[7], 561–682. 1998.
APPENDIX B
Table A2. Classification of Variables and Constructs Measured
Outcome
variables
Construct
measured
Measurement
scale Units/properties
MQOL-SIS—
stand alone item
Quality of life Continuous 0–10 VAS scale,
higher is better
MQOL-existen-
tial domain
Quality of life Continuous Mean score of 6
items, scale 0–10,
higher is better
ESAS-QOL item Quality of life Continuous 0–10 VAS scale,
lower is better
Exposure
variables
Construct
measured
Measurement
scale
Original units/
properties*
Biological variables
Body mass index Muscle wasting Continuous kg m
2
Skeletal muscle
index (skeletal
muscle mass/
total mass x
100%)
Muscle wasting Continuous %
Sarcopenia Muscle wasting Continuous No; yes
C-reactive
protein
Systemic
inflammation
Continuous mg l
1
Recalled weight
loss
Recent weight
loss
Categorical None; 2–5%;
45%
Symptoms
Gastrointestinal symptoms
Faact o2 Vomiting Categorical—
ordinal
0–4 scale, higher
is worse
ESAS nausea Nausea Continuous 0–10 VAS scale,
lower is better
ESAS appetite Appetite Continuous 0–10 VAS scale,
lower is better
Faact c6 Appetite Categorical—
ordinal
0–4 scale, higher
is better
Faact act6 Interest in food Categorical—
ordinal
0–4 scale, higher
is worse
Faact act7 Difficulty eating
rich food
Categorical—
ordinal l
0–4 scale, higher
is worse
Faact act10 Getting full
easily
Categorical—
ordinal
0–4 scale, higher
is worse
Taste and smell
Taste item Taste Categorical—
ordinal
0–2 scale, higher
is worse
Smell item Smell Categorical—
ordinal
0–2 scale, higher
is worse
Faact act 3 Taste Categorical—
ordinal
0–4 scale, higher
is worse
Pain
ESAS pain General pain Continuous 0–10 VAS scale,
lower is better
EQ-5D pain General pain Categorical—
ordinal
0–2 scale, higher
is worse
Faact act 11 Stomach pain Categorical—
ordinal
0–4 scale, higher
is worse
RAND-36—pain
subscale
General pain Continuous 0–100 scale,
higher is better
Fatigue
ESAS fatigue Fatigue Continuous 0–10 VAS scale,
lower is better
MFI—general
fatigue subscale
Fatigue Continuous 4–20 subscale,
higher is worse
RAND-36—vital-
ity subscale
Energy Continuous 0–100 scale,
higher is better
Psychological symptoms
MQOL—psycho-
logical domain
Nervousness,
being afraid,
depressed, sad
Continuous Mean score of 4
items, 0–10,
higher is better
ESAS depression Depression Continuous 0–10 VAS scale,
lower is better
ESAS anxiety Anxiety Continuous 0–10 VAS scale,
lower is better
EQ-5D depres-
sion/anxiety
Depression/
anxiety
Categorical—
ordinal
0–2 scale, higher
is worse
RAND-36—
mental health
subscale (MHI)
Nervousness, being
calm, depres-
sed, ‘blue’, happy
Continuous 0–100 scale,
higher is better
Table A2. Continued
Exposure
variables
Construct
measured
Measurement
scale
Original units/
properties*
Quality of life in advanced cancer BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2013.146 1799

Cognition and concentration
Word recall Memory Continuous 0–5, higher is
better
Mental reversal Concentration Continuous 0–5, higher is
better
Delayed recall Memory Continuous 0–5, higher is
better
Digit series
repeats forward
Memory/
concentration
Continuous 0–16, higher is
better
Digit series
repeats backwards
Memory/
concentration
Continuous 0–16, higher is
better
PBCI memory Memory Categorical—
ordinal
0–2 scale, higher
is worse
MFI—mental
fatigue
Concentration Continuous 4–20 subscale,
higher is worse
FUNCTION
Physical function
EQ-5D mobility Mobility Categorical—
ordinal
0–2 scale, higher
is worse
EQ-5D self-care Self-care Categorical—
ordinal
0–2 scale, higher
is worse
EQ-5D usual
activities
Usual activities Categorical—
ordinal
0–2 scale, higher
is worse
MFI—physical
fatigue
Physical
function
Continuous 4–20 subscale,
higher is worse
RAND-36—phy-
sical function
subscale
Physical
function
Continuous 0–100 scale,
higher is better
MFI—reduced
activity
Physical
activities
Continuous 4–20 subscale,
higher is worse
PBCI—Function
Subscale (mean
score of 5 items)
Walking, stairs,
participating in
demanding
activities, work,
driving
Continuous 0–2 subscale,
higher is worse
2 MWT distance Functional walk-
ing capacity
Continuous Metres
TUG Basic mobility Continuous Seconds
Comfortable gait
speed
Walking ability Continuous Metres/seconds
Average METS
per week
Average weekly
activity level
Continuous METS
Psychological function
PBCI coping Coping Categorical—
ordinal
0–2 scale, higher
is worse
PBCI self-esteem Self-esteem Categorical—
ordinal
0–2 scale, higher
is worse
MFI—reduced
motivation
subscale
Desire to engage
in activities
Continuous 4–20 subscale,
higher is worse
Social function
RAND-36—
social subscale
Social function Continuous 0–100 scale,
higher is better
Role function
RAND-36—role
emotional
subscale
Role function Continuous 0–100 scale,
higher is better
RAND-36—role
physical subscale
Role function Continuous 0–100 scale,
higher is better
General health perception
EQ-5D VAS GHP Continuous 0–100 VAS,
higher is better
RAND-36—GHP
Subscale
GHP Continuous 0–100 scale,
higher is better
MQOL—physical
well-being
Physical health
perception
Continuous 0–10 VAS, higher
is better
Potential con-
founding
variables
Construct
measured
Measurement
scale Units/properties
Individual characteristics
Sex Sex Binary 0 ¼female
1¼male
Age Age Continuous Years
Number of
comorbidities
Comorbidities Considered
continuous
1–7, Higher num-
ber indicates more
Individual characteristics (continued)
Cancer type Primary tumour
site
Categorical—
ordinal
Eight main
tumour sites
Educational level Proxy to Socio-
economical
status
Categorical—
ordinal
Eight levels corres-
ponding to highest
degree obtained
Nationality Cultural
influence
Categorical—
nominal
Country of birth
Social support characteristics
Marital status Social support Categorical—
nominal
Six marital
statuses
Number of
children
Social support Continuous Number of
children
Someone they
can trust and
confide in
Social support Binary No; yes
Someone who
would be able to
provide help as
long as they
would need it
Social support Binary No; yes
MQOL-support
domain
Social support Continuous Mean score of two
items, 0–10,
higher is better
Abbreviations: ESAS ¼edmonton symptom asses sment system (original version); EQ-5D ¼
EuroQoL-5D; Faact ¼functional assessment of anorexia/cachexia therapy; MFI ¼
multidimensional fatigue inventory; MQOL¼McGill quality of life questionnaire; PBCI ¼
preference-based cancer index; RAND-36 ¼RAND short form 36-item healt h survey (RAND-
36)—version1; VAS ¼visual analogue scales. Some of the items of the Facct and the MFI, as well
asallitemsoftheESAS,theEQ-5Dindex,thePBCI,andthetasteandsmellitemswererescored
so that a higher score indicates better health status. Rescoring for some variables took place
after the examination of the frequencies to accou nt for categories with no or little observations.
Table A2. Continued
Exposure
variables
Construct
measured
Measurement
scale
Original units/
properties*
Table A2. Continued
Exposure
variables
Construct
measured
Measurement
scale
Original units/
properties*
BRITISH JOURNAL OF CANCER Quality of life in advanced cancer
1800 www.bjcancer.com | DOI:10.1038/bjc.2013.146