BASIC SCIENCE REVIEW
Measuring Health-related Quality of Life
Gordon H. Guyatt, MD; David H. Feeny, PhD; and Donald L. Patrick, PhD, MSPH
Clinicians and policymakers are recognizing the im›
portance of measuring health-related quality of life
(HRQL) to inform patient management and policy deci›
sions. Self- or interviewer-administered questionnaires
can be used to measure cross-sectional differences in
quality of life between patients at a point in time
(discriminative instruments) or longitudinal changes in
HRQL within patients during a period of time (evaluative
instruments). Both discriminative and evaluative instru›
ments must be valid (really measuring what they are
supposed to measure) and have a high ratio of signal to
noise (reliability and responsiveness, respectively). Re›
liable discriminative instruments are able to reproduc-
ibly differentiate between persons. Responsive evalua›
tive measures are able to detect important changes in
HRQL during a period of time, even if those changes are
small. Health-related quality of life measures should
also be interpretable that is, clinicians and policymak›
ers must be able to identify differences in scores that
correspond to trivial, small, moderate, and large differ›
ences.
Two basic approaches to quality-of-life measure›
ment are available: generic instruments that provide a
summary of HRQL; and specific instruments that focus
on problems associated with single disease states,
patient groups, or areas of function. Generic instru›
ments include health profiles and instruments that
generate health utilities. The approaches are not mutu›
ally exclusive. Each approach has its strengths and
weaknesses and may be suitable for different circum›
stances. Investigations in HRQL have led to instru›
ments suitable for detecting minimally important effects
in clinical trials, for measuring the health of populations,
and for providing information for policy decisions.
Annals of Internal Medicine. 1993;118:622-629.
From McMaster University, Hamilton, Ontario, Canada; and
the University of Washington, Seattle, Washington. For cur›
rent author addresses, see end of text.
What Is Health-related Quality of Life?
Health status, functional status, and quality of life
are three concepts often used interchangeably to refer
to the same domain of "health" (1). The health domain
ranges from negatively valued aspects of life, including
death, to the more positively valued aspects such as
role function or happiness. The boundaries of definition
usually depend on why one is assessing health as well
as the particular concerns of patients, clinicians, and
researchers. We use the term health-related quality of
life (HRQL) because widely valued aspects of life exist
that are not generally considered as "health," including
income, freedom, and quality of the environment. Al›
though low or unstable income, the lack of freedom, or
a low-quality environment may adversely affect health,
these problems are often distant from a health or med›
ical concern. Clinicians focus on HRQL, although when
a patient is ill or diseased, almost all aspects of life can
become health related.
Why Measure HRQL?
HRQL is important for measuring the impact of
chronic disease (2). Physiologic measures provide infor›
mation to clinicians but are of limited interest to pa›
tients; they often correlate poorly with functional ca›
pacity and well-being, the areas in which patients are
most interested and familiar. In patients with chronic
heart and lung disease, exercise capacity in the labora›
tory is only weakly related to exercise capacity in daily
life (3). Another reason to measure HRQL is the com›
monly observed phenomena that two patients with the
same clinical criteria often have dramatically different
responses. For example, two patients with the same
range of motion and even similar ratings of back pain
may have different role function and emotional well-
being. Although some patients may continue to work
without major depression, others may quit their jobs
and have major depression.
These considerations explain why patients, clinicians,
and health care administrators are all keenly interested
in the effects of medical interventions on HRQL (4).
Administrators are particularly interested in HRQL be›
cause the case mix of patients affects use and expendi›
ture patterns, because increasing efforts exist to incor›
porate HRQLs as measures of the quality of care and of
clinical effectiveness, and because payers are beginning
to use HRQL information in reimbursement decisions.
Abbreviations
HRQL health-related quality of life
MOS Medical Outcome Study
622 ' 1993 American College of Physicians

Table 1. Modes of Administration of HRQL Measures
Mode of Administration Strengths Weaknesses
Interviewer Maximizes response rate Requires many resources, training of interviewers
Few, if any, missing items May reduce willingness to acknowledge problems
Minimizes errors of misunderstanding Limits format of instrument
Telephone Few, if any, missing items
Minimizes errors of misunderstanding
Less resource intensive than
interviewer-administered mode
Self Minimal resources required Greater likelihood of low-response rate, missing
items, misunderstanding
Surrogate responders Reduces stress for target group (very Perceptions of surrogate may differ from target
elderly or sick) group
The Structure of HRQL Measures
Some HRQL measures consist of a single question
that essentially asks "How is your quality of life?" (5)
This question may be asked in a simple or a sophisti›
cated fashion, but either way it yields limited informa›
tion. More commonly, HRQL instruments are question›
naires made up of a number of items or questions.
These items are added up in a number of domains (also
sometimes called dimensions). A domain or dimension
refers to the area of behavior or experience that we are
trying to measure. Domains might include mobility and
self-care (which could be further aggregated into phys›
ical function), or depression, anxiety, and well-being
(which could be aggregated to form an emotional-func›
tion domain). For some instruments, investigators do
rigorous valuation exercises in which the importance of
each item is rated in relation to the others. More often,
items are equally weighted, which assumes that their
value is equal.
Modes of Administration
The strengths and weaknesses of the different modes
of HRQL administration are summarized in Table 1.
Health-related quality-of-life questionnaires are either
administered by trained interviewers or self-adminis›
tered. The former method is resource intensive but en›
sures compliance, decreases errors, and decreases miss›
ing items. The latter approach is much less expensive
but increases the number of missing subjects and in›
creases missing responses. A compromise between the
two approaches is to have instruments completed with
supervision. Another compromise is the phone inter›
view, which decreases errors and decreases missing
data but dictates a relatively simple questionnaire struc›
ture. Investigators have done initial experiments with
computer-administration of HRQL measures, but this is
not yet a common method of questionnaire administra›
tion.
Investigators sometimes use a surrogate respondent
to predict results that would be obtained from the pa›
tient. For instance, McKusker and Stoddard (6) were
interested in what patients might score on a general,
comprehensive measure of HRQL the Sickness Im›
pact Profile when they were too ill to complete the
questionnaire. The investigators used a surrogate to re›
spond on behalf of the patient but wanted assurance
that surrogate responses would correspond to what pa›
tients would have said had they been capable of an›
swering. They administered the Sickness Impact Profile
to terminally ill patients who were still capable of com›
pleting the questionnaire and to close relatives of the
respondents. The correlation between the two sets of
responses was 0.55, and the difference between the two
pairs of responses was greater than 6 on a 100-point
scale for 50% of the patients. The results provide only
moderate support for the validity of surrogate responses
to the Sickness Impact Profile.
These results are consistent with other evaluations of
ratings by patients and proxies. In general, the corre›
spondence between respondent and proxy response to
HRQL measures varies depending on the domain as›
sessed and the choice of proxy. Proxy reports of more
observable domains, such as physical functioning and
cognition, are more highly correlated with reports from
the patients themselves. For functional limitations,
proxy respondents tend to consider patients more im›
paired (they overestimate patient dysfunction relative to
the patients themselves). This is particularly character›
istic of those proxies with the greatest contact with the
respondent (7). For other sorts of morbidity, patients
tend to report the most problems, followed by close
relatives, and clinicians report the least. These findings
have important clinical implications because they sug›
gest that clinicians should concentrate on careful ascer›
tainment of the reported behaviors and perceptions of
patients themselves, and they should limit the infer›
ences they make on the basis of the perceptions of the
caregivers.
What Makes a Good HRQL Instrument?
Measuring at a Point in Time versus Measuring Change
The goals of HRQL measures include differentiating
between people who have a better HRQL and those
who have a worse HRQL (a discriminative instrument)
as well as measuring how much the HRQL has changed
(an evaluative instrument) (8). The construction of in›
struments for these two purposes is different. If we
want to discriminate between those with and without
thyroid disease, we would be unlikely to include fatigue
as an item because fatigue is too common among people
who do not have thyroid disease. On the other hand, in
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