Development of physical and mental health summary scores
from the patient-reported outcomes measurement information
system (PROMIS) global items
Ron D. Hays Æ Æ Jakob B. Bjorner Æ Æ Dennis A. Revicki Æ Æ
Karen L. Spritzer Æ Æ David Cella
Accepted: 28 May 2009/Published online: 19 June 2009
? The Author(s) 2009. This article is published with open access at Springerlink.com
efficient way of gathering general perceptions of health.
These items provide useful summary information about
health and are predictive of health care utilization and
Analyses of 10 self-reported global health items
obtained from an internet survey as part of the Patient-
Reported Outcome Measurement Information System
(PROMIS) project. We derived summary scores from the
global health items. We estimated the associations of
the summary scores with the EQ-5D index score and the
PROMIS physical function, pain, fatigue, emotional dis-
tress, and social health domain scores.
Exploratory and confirmatory factor analyses
supported a two-factor model. Global physical health
(GPH; 4 items on overall physical health, physical func-
tion, pain, and fatigue) and global mental health (GMH; 4
items on quality of life, mental health, satisfaction with
social activities, and emotional problems) scales were
The use of global health items permits an
created. The scales had internal consistency reliability
coefficients of 0.81 and 0.86, respectively. GPH correlated
more strongly with the EQ-5D than did GMH (r = 0.76 vs.
0.59). GPH correlated most strongly with pain impact
(r = -0.75) whereas GMH correlated most strongly with
depressive symptoms (r = -0.71).
Two dimensions representing physical and
mental health underlie the global health items in PROMIS.
These global health scales can be used to efficiently sum-
marize physical and mental health in patient-reported
Item response theory ? EQ-5D
Global health ? PROMIS ?
Assessment of health-related quality of life (HRQOL)—
that is, functioning and well-being in physical, mental, and
social domains of life–has been shown to be useful in
screening for disability and in improving communication
between patients and clinicians [1, 2]. Generic HRQOL
profile measures use multiple items to assess each of
multiple domains of health. To reduce response burden,
short-form HRQOL measures such as the SF-36 health
survey are widely used . Although their brevity makes
short-form measures practical for widespread use, even the
SF-36 requires 7–10 min to complete.
The Dartmouth COOP Charts were designed to provide
the briefest possible measure of HRQOL . This instru-
ment consists of global items (‘‘chart’’) to represent each
domain of health. These items are administered using five
response choices . For example, one of the charts
assesses overall health using the single item, ‘‘How would
R. D. Hays (&) ? K. L. Spritzer
Department of Medicine, UCLA, Los Angeles, CA, USA
J. B. Bjorner
QualityMetric Incorporated, 640 George Washington Highway,
Suite 201, Lincoln, RI 02865, USA
D. A. Revicki
Center for Health Outcomes Research, United BioSource
Corporation, 7101 Wisconsin Ave., Suite 600,
Bethesda, MD 20814, USA
Department of Medical Social Sciences, Feinberg School
of Medicine, Northwestern University, Chicago, IL, USA
Qual Life Res (2009) 18:873–880
you rate your health in general? (Excellent, Very good,
Good, Fair, Poor.)’’ The Charts have the advantage of ease
of administration and scoring but tend to be less precise
and specific than multi-item scales. The Charts are one of
the original examples of the use of global health items to
assess multiple HRQOL domains.
Global health items are evaluations of health in general
rather than specific elements of health. Global items allows
respondents to weigh together different aspects of health to
arrive at a ‘bottom-line’’ indicator of their health status.
They allow an efficient assessment of self-reported health.
Global health items are predictive of important future
events such as health care utilization and mortality .
The aim of this study was to evaluate global items
representing physical health, pain, fatigue, mental health,
social health, and overall health. These domains reflect the
health framework used by the Patient-Reported Outcomes
Measurement Information System (PROMIS; see www.
nihpromis.org) . We examine the individual items and
assess possible aggregation of them into underlying
dimensions of health as measured in PROMIS. We first
evaluate whether scoring the items together as a single
summary scale is supported empirically. Then we examine
alternatives that better reflect the data.
The PROMIS item banks were administered via web-based
survey to a national internet panel maintained by Polime-
trix (now YouGovPolimetrix; see www.polimetrix.com).
The field test involved administering the item banks from
five domains (i.e., physical functioning, pain, fatigue,
emotional distress, social health) to selected participants.
We randomly assigned some respondents to complete full
item banks, that is, all the items within a defined domain-
specific bank such as physical function or fatigue. We
randomly assigned other respondents to sets of 7 consec-
utive items for each of 14 hypothesized sub-domains from
the 5 health domains.
The 10 global health items include ratings of the five core
PROMIS domains and ratings that cut across domains
(Appendix). The PROMIS global health item set includes
the most widely used self-rated health item (global01).
Previous research has shown that this item taps both
physical health and mental health but reflects physical
health more than mental health, especially for those with
low income . PROMIS includes a single item that
provides a pure rating of physical health (global03) and
another item for mental health (global04). Also included is
an overall quality of life item (global02) that is a very
strong indicator of mental health (see e.g., Lorenz et al.
). The remaining items provide global ratings of
physical function (global06), fatigue (global08), pain
(global07), emotional distress (global10), and social health
(global05 and global09).
We administered all of the items except the rating of
pain on average (global07) using five-category response
scales (see Appendix). We recoded global07 from the 0–10
scale to 5 categories based on grouping of 0–10 response
scales for the Sheehan Disability Scale and the Flushing
Symptom Questionnaire  as follows: 0 = 1; 1–3 = 2;
4–6 = 3; 7–9 = 4; 10 = 5.
We also administered the EQ-5D survey, a widely used
generic HRQOL preference-based measure, to study par-
ticipants. We examine the empirical associations of the
PROMIS global items with the EQ-5D. For this purpose,
we derived the EQ-5D preference-based index score using
the US general population weights . The EQ-5D is
anchored by 0 (dead) and 1 (perfect health). The lowest
possible score for the EQ-5D is -0.11, indicating a health
state rated worse than being dead by the sample of 4,048
people in the US valuation sample.
The PROMIS sample was selected to be comparable to
distributions of gender, age groups, race/ethnicity (white/
African–American/Hispanic/other) and education (high
school or less versus more than high school) based on the
2000 US census data . We identified study participants
from the Polimetrix internet panel.
Because of the number of item banks being tested, we
employed a complex data collection strategy. This strategy
included two arms and a total sample size of 21,133 (see
Fig. 1). Polimetrix recruited a total of 19,601 subjects; we
recruited the remaining 1,532 subjects from the PROMIS
arm, we administered randomlyselected 7-item blocks from
individuals. The PROMIS research sites and the Polimetrix
sample included both community and clinical samples.
The clinical samples included persons with heart disease
(n = 1,156), cancer (n = 1,754), rheumatoid arthritis
(n = 557), osteoarthritis (n = 918), psychiatric disorders
(n = 1,193),chronic obstructive
(n = 1,214), spinal cord injury (n = 531), and other
conditions (n = 560).
Table 1 provides a summary of sample characteristics.
The average age was 53 and 52% were female. The
874Qual Life Res (2009) 18:873–880
majority were non-Hispanic white (80%); 9% were Latino
and 9% non-Hispanic black. The sample was well edu-
cated—only 19% had only a high school degree or less.
We estimated polyserial correlations of the global items
with the EQ-5D. In addition, we examined item-scale cor-
relations and conducted confirmatory categorical factor
analysis (based on polychoric correlations) to evaluate
whether the 10 global health items could be combined into a
single unidimensional scale. Next, we performed explor-
atory factor analysis on the matrix of polychoric correla-
tions to identify the number of underlying dimensions. We
evaluated the resulting two factors by estimating item-scale
correlations and internal consistency reliability. We used
Mplus 5.1 software  to estimate confirmatory categor-
ical factor analysis models, specifying weighted least
squares mean and variance estimation. Because of our large
sample size we do not rely on the chi-square statistic to
evaluate the acceptability of the models. We estimated
practical fit of the models using the confirmatory fit index
(CFI), Tucker–Lewis index (TLI), and the root mean square
error of approximation (RMSEA). We averaged items to
form physical and mental health composites and estimated
associations of these composites with the EQ-5D and the
nine PROMIS domain scores (physical functioning, pain
behavior, pain impact, fatigue, anxiety, anger, depressive
symptoms, satisfaction with discretionary social activities,
satisfaction with social roles). Finally, we estimated item
threshold and discrimination parameters for the final physi-
[12, 13]. Based on the item parameters we calculated item
information, the contribution of each item to overall test
precision . As an estimate of the contribution of each
item to overall test precision, we weighted item-level
information across the score distribution of our sample.
Item-scale correlations for the 10 global health items ranged
from 0.53 (global7: rating of pain) to 0.80 (global09: satis-
faction with social roles) and internal consistency reliability
was 0.92. However, the single-factor confirmatory categor-
ical factor analysis model for all 10 items was statistically
rejectable (v2= 19,619.82, df = 15, P B 0.001) and did
not fit the data very well (CFI = 0.927; TLI = 0.961;
RMSEA = 0.249).
The eigenvalues from a principal components analysis
of the 10 global items were 6.25, 1.20, 0.75, 0.44, 0.39,
0.30, 0.22, 0.20, 0.18, and 0.05. The scree plot and parallel
analysis number of factor criteria suggested two underlying
Research sites (n=329 general population) Polimetrix (n=6,676 general population)
Research sites (n=1,203) Polimetrix (n=12,925)
General population (n=400)Clinical (n=803) General population (n=5,845) Clinical (n=7,080)
Full Bank Arm (n=7,005)
Block Arm (n=14,128)
Fig. 1 PROMIS data collection
(n = 21,133)
Table 1 Sample characteristics (n = 21,133)
Age (mean and range)53 (18–100)
High school graduate
Body mass index (median and % obese)27 (35% obese)
No chronic conditions19%
Note Chronic conditions assessed included hypertension, angina,
coronary artery disease, heart failure, heart attack, stroke, liver dis-
ease, kidney disease,arthritis
migraines, asthma, chronic obstructive pulmonary disease, diabetes,
cancer, depression, anxiety, alcohol or drug problems, sleep disorder,
HIV/AIDS, spinal cord injury, multiple sclerosis, Parkinson’s disease,
epilepsy, and amyotrophic lateral sclerosis
Qual Life Res (2009) 18:873–880 875
dimensions for the 10 items. We performed an exploratory
factor analysis and found support for a physical health and
mental health factor (see Table 2). Satisfaction with dis-
cretionary social activities (global05) loaded on mental
health whereas satisfaction with social roles (global09)
loaded on both physical and mental health (as did global02:
quality of life; and global08: fatigue). The estimated
correlation between the physical and mental health factors
was 0.63. These results were also supported by our con-
firmatory categorical factor analysis, but three residual
correlations were added to obtain acceptable model fit; see
Table 2 (global01 with global03 r = 0.14, global04 with
global10 r = 0.14, and global08 with global10 r = 0.15;
v2= 5,295.66, df = 17, P\0.0001; CFI = 0.98; TLI =
0.99, RMSEA = 0.12). The estimated correlation between
the physical and mental health factors was 0.69.
Based on the exploratory factor analysis, we evaluated a
physical health scale with the 5 items loading highest on the
physical health factor. Global09 (satisfaction with social
roles) was excluded because it correlated about equally with
physical health items ranged from 0.57 (global07: rating of
rating of physical health). All 5 items correlated higher with
fit a single-factor categorical confirmatory factor analytic
model for the five physical health items and found that it was
statistically rejectable (v2= 3,060.81, P\0.001) and
showed less than adequate practical fit according to the
RMSEA index (CFI = 0.991; RMSEA = 0.220). By adding
a residual correlation (r = 0.29) between global01 (rating of
general health) and global03 (rating of physical health) to the
initial model, we found that the fit of the model improved
significantly (v2= 2,248.57, df = 1, P\0.001) and the
practical fit indices also improved (v2= 419.56, P\0.001;
CFI = 0.999; TLI = 0.998; RMSEA = 0.081).
We also evaluated a mental health scale with 4 items.
Three of these items correlated most highly with the mental
health scale. The fourth item, global02 (quality of life),
correlated about equally with physical and mental health,
but was also included because of prior evidence that it is
primarily an indicator of mental health. Item-scale correla-
0.64 (global10: emotional problems) to 0.78 (global04:
rating of mental health). One item (global09, satisfaction
with social roles) had higher correlation with the global
physical health scale than with the mental health scale; the
4 mental health items correlated strongest with the mental
health scale. The single-factor categorical confirmatory
factor analytic model we fit for these 4 mental health
items was statistically rejectable (v2= 1,616.80, df = 2,
P B 0.001), and had mixed results in terms of practical fit
(CFI = 0.983; TLI = 0.975; RMSEA = 0.196). When we
added a residual correlation (r = 0.16) between global04
(rating of mental health) and global10 (bothered by
emotional problems) to the initial model, the fit improved
significantly (v2= 1,114.27, df = 1, P\0.001) and the
practical fit of the model improved (v2= 151.222,
P B 0.001; CFI = 0.998; TLI = 0.995; RMSEA = 0.084).
Based on these results, we formed two-four-item scales
by averaging together the items scored on a 1–5 possible
range. Our physical health items included global03 (phys-
ical health), global06 (physical function), global07 (pain)
and global08 (fatigue). Our mental health items included
global02 (quality of life), global04 (mental health), glo-
bal05 (satisfaction with discretionary social activities), and
global10 (emotional problems). The global physical health
(GPH) scale excluded global01 (general health) because of
its substantial residual correlation with global03 (physical
health). We retained global03 in the scale rather than glo-
bal01 to emphasize the physical nature of the construct. The
GPH had an internal consistency reliability of 0.81
Table 2 Two factor pattern for global health items (standardized regression coefficients)
ItemsDescriptionExploratory factor analysisConfirmatory factor analysis
Quality of life0.466
Note Bold entries denote largest loading on the factors for that item
876 Qual Life Res (2009) 18:873–880
(mean = 3.79, SD = 0.76). We excluded global09 (satis-
faction with social roles) from the global mental health
(GMH) scale because of its higher correlation with the GPH
scale. The GMH had an internal consistency reliability of
0.86 (mean = 3.60, SD = 0.89). The two scales were sub-
stantially inter-correlated (r = 0.63). In addition, we found
that GPH correlated more strongly with the EQ-5D than did
the GMH (r = 0.76 vs. 0.59). The R-square in a regression
of the EQ-5D on the GPH and GMH was 0.60, indicating
that the PROMIS global health composites share 60% of
variance in common with the EQ-5D.
Correlations of the global health items and GPH and
GMH with the nine PROMIS domain scores and the
EQ-5D are given in Table 3. The largest correlations for
global01 (rating of general health), global02 (quality of
life), global03 (rating of physical health), global08 (rating
of fatigue), and global09 (satisfaction with social roles)
were with the fatigue domain. Global04 (rating of mental
health), global05 (satisfaction with discretionary social
activities) and global10 (emotional problems) correlated
most strongly with the depressive symptoms domain.
Global06 (carry out everyday physical activities) corre-
lated most strongly with physical functioning whereas
global07 (rating of pain) correlated highest with pain
impact. The GPH correlated most strongly with pain
impact (r = -0.75), fatigue (r = -0.73), and physical
functioning (r = 0.71). GMH correlated most strongly with
depressive symptoms (r = -0.71), fatigue (r = -0.68), and
anxiety (r = -0.65).
Correlations of the global items with the EQ-5D ranged
from 0.51 to 0.77. The largest correlations with the EQ-5D
were for the global ratings of pain, physical functioning,
and satisfaction with social roles. Our regression of the EQ-
5D on the global items revealed that all items except two
(global03: rating of physical health; global05: satisfaction
with discretionary social activities) had significantly
unique associations (R-square = 0.64).
We estimated item parameters from the graded response
model for the 4 global physical health items (Table 4) and
4 global mental health items (Table 5). The range of item
threshold values indicates satisfactory coverage of the
underlying latent trait from *-4.0 to 2.0 for Physical
Health and between -3.0 and 1.5 for Mental Health.
Global06 (carry out everyday physical activities) had the
Table 3 Correlations of global items with PROMIS domains and EQ-5D
-0.38-0.27-0.39 0.43 0.510.65
0.34-0.38-0.43-0.55-0.50-0.40 0.560.53 0.51
0.54-0.52-0.59-0.49-0.34-0.52 0.56 0.640.67
Note All P values\.0001; Highest correlations with PROMIS domains in each row are in bold. Polyserial correlations are provided in the last
Global01, In general, would you say your health is…; Global02, In general, would you say your quality of life is…; Global03, In general, how
would you rate your physical health?; Global04, In general, how would you rate your mental health?; Global05, In general, how would you rate
your satisfaction with social activities and relationships?; Global06, To what extent are you able to carry out your everyday physical activities;
Global07, How would you rate your pain on average?; Global08, How would you rate your fatigue on average?; Global09, In general, please rate
how well you carry out your usual social activities and roles; Global10, How often have you been bothered by emotional problems?; GPH,
Global physical health scale; GMH, Global mental health scale
Table 4 Global physical health scale item parameters (graded
response model) and item information
Item 1: Global03, In general, how would you rate your physical
health?; Item 2: Global06, To what extent are you able to carry out
your everyday physical activities?; Item 3: Global07, How would you
rate your pain on average?; Item 4: Global08, How would you rate
your fatigue on average?
Qual Life Res (2009) 18:873–880877
highest slope (a parameter in Table 4) and the largest
information for the physical health items whereas global04
(rating of mental health) had the largest information for the
mental health items. We found the lowest item information
for items phrased to elicit ratings of undesirable domains of
health (pain, fatigue, emotional problems).
The results of our study provide some support for the con-
struct validity of the global health items based on their cor-
relations with comparable multi-item scales from PROMIS.
For example, the global rating of mental health (global04)
correlated most strongly with the PROMIS depressive
symptoms scale; the global rating of fatigue (global08)
correlated strongest with the PROMIS fatigue scale.
In addition, our exploratory factor analyses suggested
two underlying dimensions for the global health items. One
dimension is defined by indicators of primarily physical
health and the other by indicators of mental health. Similar
underlying factors have been found in previous research
[14–16]. Moreover, the correlation we estimated between
the GPH and GMH (r = 0.63) in this study was very
similar to correlations between physical and mental health
factors derived from the SF-36 (e.g., r = 0.62 in Farivar
et al. ) and other measures of HRQOL  using
oblique rotation. We recommend scoring the scales using 8
items, but also scoring the remaining 2 items as single
items separately: Global01 (General health) and Global09
(satisfaction with social roles).
A major advantage of the global health scales developed
here is the brevity of the resulting measure for gathering
summary information about health. For the two scales,
each of which had 4 items, we obtained reliabilities of 0.81
and 0.86; together they require about 2 min to complete. In
contrast, the SF-36 takes about 7–10 min to administer and
the estimated reliabilities are about 0.88–0.93 for the SF-36
physical and mental health composites . The SF-12TM
 and SF-8TM Health Surveys have completion
times and reliabilities that are comparable to the current
survey. Future head-to-head comparisons of the present
instruments and these instruments would be beneficial.
Although the physical and mental health scales are
valuable for summarizing health, if a study shows
improvement in one of the summary measures and decre-
ment in the other, drawing an overall conclusion can be
difficult. Moreover, attrition of study participants over time
because they have died presents challenges for longitudinal
comparisons based on these global scores because of the
bias of dropping those who die from the analysis. Prefer-
ence-based measures are designed to derive a single sum-
mary score that links morbidity and mortality by anchoring
the metric so that 0 is ‘‘as bad as being dead’’ and 1
represents ‘‘perfect health.’’ This study showed noteworthy
associations of the global health scores with the EQ-5D
preference-based score; 60% of the variance was shared in
common. A separate paper derives equations estimating
EQ-5D index scores from these composite scores .
Investigators can use the 10 global health items in future
studies to assess global physical and mental health. The
items are available as part of the PROMIS item banks at:
http://www.nih.promis.org. In addition, the items can be
examined separately to provide specific information about
perceptions of physical function, pain, fatigue, emotional
distress, social health and general perceptions of health.
Future studies are needed to evaluate the relative validity
of the global scales compared with physical and mental
health composites derived from other measures such as the
SF-12 and SF-36.
Information System (PROMIS) is a US. National Institutes of Health
(NIH) Roadmap initiative to develop a computerized system mea-
suring patient-reported outcomes in respondents with a wide range of
chronic diseases and demographic characteristics. PROMIS was
funded by cooperative agreements to a Statistical Coordinating Center
U01AR52177) and six Primary Research Sites (Duke University, PI:
Kevin Weinfurt, PhD, U01AR52186; University of North Carolina,
PI: Darren DeWalt, MD, MPH, U01AR52181; University of Pitts-
burgh, PI: Paul A. Pilkonis, PhD, U01AR52155; Stanford University,
PI: James Fries, MD, U01AR52158; Stony Brook University, PI:
Arthur Stone, PhD, U01AR52170; and University of Washington, PI:
Dagmar Amtmann, PhD, U01AR52171). NIH Science Officers on
this project are Deborah Ader, Ph.D., Susan Czajkowski, PhD,
Lawrence Fine, MD, DrPH, Louis Quatrano, PhD, Bryce Reeve, PhD,
William Riley, PhD, and Susana Serrate-Sztein, PhD. Ron D. Hays
was also supported by the UCLA Resource Center for Minority Aging
Research/Center for Health Improvement in Minority Elderly
(P30AG021684), and the UCLA/DREW Project EXPORT, National
Institutes of Health, National Center on Minority Health & Health
Disparities (P20MD000148 and P20MD000182). This manuscript
was reviewed by the PROMIS Publications Subcommittee prior to
external peer review. See the web site at www.nihpromis.org for
additional information on the PROMIS cooperative group.
The Patient-Reported Outcomes Measurement
PI: David Cella,PhD,
Table 5 Global mental health scale item parameters (graded
response model) and item information
a b1 b2b3 b4 Information
Item 1: Global02, In general, would you say your quality of life is…;
Item 2: Global04, In general, how would you rate your mental
health?; Item 3: Global05, In general, how would you rate your sat-
isfaction with social activities and relationships?; Item 4: Global10,
How often have you been bothered by emotional problems?
878 Qual Life Res (2009) 18:873–880
Global health items
Variable nameItem contextItem stemResponses
Global01In general, would you say your health is:5 = Excellent
4 = Very good
3 = Good
2 = Fair
1 = Poor
Global02 In general, would you say your quality of life
5 = Excellent
4 = Very good
3 = Good
2 = Fair
1 = Poor
Global03In general, how would you rate your
5 = Excellent
4 = Very good
3 = Good
2 = Fair
1 = Poor
Global04 In general, how would you rate your mental
health, including your mood and your
ability to think?
5 = Excellent
4 = Very good
3 = Good
2 = Fair
1 = Poor
Global05 In general, how would you rate your
satisfaction with your social activities and
5 = Excellent
4 = Very good
3 = Good
2 = Fair
1 = Poor
Global06 To what extent are you able to carry out your
everyday physical activities such as
walking, climbing stairs, carrying
groceries, or moving a chair?
5 = Completely
4 = Mostly
3 = Moderately
2 = A little
1 = Not at all
Global07 In the past 7 days How would you rate your pain on average?0 = 0 No pain; 1 = 1; 2 = 2; 3 = 3; 4 = 4;
5 = 5; 6 = 6; 7 = 7; 8 = 8; 9 = 9;
10 = 10 worst pain imaginable
Global08In the past 7 daysHow would you rate your fatigue on
1 = None
2 = Mild
3 = Moderate
4 = Severe
5 = Very severe
Global09In the past 7 days In general, please rate how well you carry
out your usual social activities and roles.
(This includes activities at home, at work
and in your community, and
responsibilities as a parent, child, spouse,
employee, friend, etc.)
5 = Excellent
4 = Very good
3 = Good
2 = Fair
1 = Poor
Global10In the past 7 daysHow often have you been bothered by
emotional problems such as feeling
anxious, depressed or irritable?
1 = Never
2 = Rarely
3 = Sometimes
4 = Often
5 = Always
Qual Life Res (2009) 18:873–880 879
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