r Health Research and Educational Trust
DOI: 10.1111/j.1475-6773.2005.00438.x
Methods
Development and Testing of a Short
Form of the Patient Activation Measure
Judith H. Hibbard, Eldon R. Mahoney, Jean Stockard, and
Martin Tusler
Objective. The Patient Activation Measure (PAM) is a 22-item measure that assesses
patient knowledge, skill, and confidence for self-management. The measure was de-
veloped using Rasch analyses and is an interval level, unidimensional, Guttman-like
measure. The current analysis is aimed at reducing the number of items in the measure
while maintaining adequate precision.
Study Methods. We relied on an iterative use of Rasch analysis to identify items that
could be eliminated without loss of significant precision and reliability. With each item
deletion, the item scale locations were recalibrated and the person reliability evaluated
to check if and how much of a decline in precision of measurement resulted from the
deletion of the item.
Data Sources. The data used in the analysis were the same data used in the devel-
opment of the original 22-item measure. These data were collected in 2003 via a tele-
phone survey of 1,515 randomly selected adults.
Principal Findings. The analysis yielded a 13-item measure that has psychometric
properties similar to the original 22-item version. The scores for the 13-item measure
range in value from 38.6 to 53.0 (on a theoretical 0–100 point scale). The range of values
is essentially unchanged from the original 22-item version. Subgroup analysis suggests
that there is a slight loss of precision with some subgroups.
Conclusions. The results of the analysis indicate that the shortened 13-item version is
both reliable and valid.
Key Words. Patient activation, self-management, consumer roles in health care
A previous publication described the development and testing of the Patient
Activation Measure (PAM), which assesses patient self-reported knowledge,
skill, and confidence for self-management of one’s health or chronic condition
(Hibbard et al. 2004). The 22-item PAM was developed using Rasch psycho-
metric methods and is an interval level, unidimensional, Guttman-like meas-
ure. This current analysis is aimed at reducing the number of items in the
measure without significant loss of precision. The methodology used to create
1918
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Development and Testing of a Shorter Patient Activation Measure 1919
the short form (PAM-13) is described and the psychometric properties of the
short-form PAM are compared with those of the original 22-item PAM.
Finally, the potential clinical and research applications of the short form
measure are discussed.
BACKGROUND
Patients make many choices in their day-to-day lives that have major impli-
cations for their health and their need for care. Chronic disease patients often
must follow complex treatment regimens, monitor their conditions, make life-
style changes, and make decisions about when they need to seek professional
care and when they can handle a problem on their own. Effectively functioning
in the role of self-manager, particularly when living with one or more chronic
illnesses, requires a high level of knowledge, skill, and confidence.
Imagine clinicians trying to treat a patient completely blinded to the
patient’s record and list of clinical symptoms. Yet, when clinicians encourage
patient engagement in their care, they do so blinded to any information on the
patient’s capabilities for taking on a self-management role. What often results
is a ‘‘one size fits all’’ patient education approach. If, however, clinicians had
information on their patients’ level of knowledge and skill to self-manage, they
could target self-care education and support to individual patient needs and
presumably be more effective in supporting patient’s self-management.
Making the suggestion to lose 20 pounds, start going to the gym, and
regularly take their hypertension medication to a patient who has little un-
derstanding that they even have a chronic illness, the nature of that illness, or
that they must play a part in managing it, is unlikely to result in the desired
outcome. However, starting with appropriate goals that fit the patient’s level of
activation, and working toward increasing activation step by step, patients can
experience small successes and steadily build up the confidence and skill for
effective self-management (Bandura 1991; Battersby et al. 2003).
Address correspondence to Judith Hibbard, Dr. P.H., Professor Health Policy, Department of
Planning, Public Policy, & Management, University of Oregon, Eugene, OR 97403-1209. Eldon
Mahoney, Ph.D., is Director, Survey Research and Development, Quality Improvement Division,
PeaceHealth, Bellingham, WA. Jean Stockard, Ph.D., Professor, and Martin Tusler, M.S., Data
Analyst, are with the Department of Planning, Public Policy, & Management, University of Or-
egon, Eugene, OR.
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1920 HSR: Health Services Research 40:6, Part I (December 2005)
Supporting patients in their role as self-managers is an essential element
of high quality chronic illness care. As with other dimensions of quality, the
ability to measure is a prerequisite to improvement. The recent Institute of
Medicine Summit on Crossing the Quality Chasm suggested new directions in
quality measurement, which are consistent with the use of the PAM for this
purpose:

 First, measurement should focus on the patient, including patient
experience and patient outcomes. This could include intermediate
patient outcomes such as knowledge and skills for self-management.
This approach acknowledges that the patient should be at the center
of measurement and care processes, and emphasizes that patients are
essential players in their own health outcomes.

 Second, measurement should be integrated into the care delivery
process and improve the care of the patient being measured. That is,
by measuring intermediate patient outcomes, there is the opportu-
nity to improve care for that patient, as well as assess quality across
groups of patients.

 Finally, measurement should be longitudinal and capture what hap-
pens to patients over time. It may be necessary to measure at more
than one point in time to understand how care is affecting patients’
experiences, their capabilities for self-management, and their quality
of life, health, and ability to function. (Institute of Medicine 2004).
A measure of patient activation could also possibly be used to manage
whole patient populations. For example, delivery systems can stratify their
enrolled patient populations, not only by health risk level (level of resource
consumption), as often done, but also by their activation level, allowing for
early intervention with patients who lack the skills to self-manage before they
inevitably move to a higher health risk group.
A shorter version of the PAM would greatly enhance the feasibility of
measuring activation in a clinical setting and would make survey administra-
tion much less burdensome and costly. To this end creation of the short form
PAM was undertaken.
METHODS
The optimum set of items in a measure have four characteristics: each item
contributes substantive content central to the construct being measured; the
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Development and Testing of a Shorter Patient Activation Measure 1921
items are well spaced along the measurement scale from easy to difficult items;
each item’s location on the measurement scale is precisely located (a small
standard error of measurement); each item contributes sufficiently unique
information about the amount of the construct (not redundant) to justify the
response burden created by inclusion of the item. These criteria guided the
item reduction process.
A telephone survey of 1,5151 randomly selected adults in the U.S., aged
45 years and older, was carried out in 2003. Respondents were selected via a
random digit dial selection and a screening question to determine age eligi-
bility. No other eligibility requirement was used. A 48 percent response rate
was achieved with a protocol of a minimum of 12 call backs. Respondents
ranged in age from 45 to 97, with 66 percent of the sample under the age of 65.
Half the sample had a high school education or less and 32 percent had a
household income of under $25,000. Seventy-nine percent of the sample re-
ported at least one chronic disease. These numbers compare well with the
2000 U.S. Census in which 53 percent of those aged 45 years and older had a
high school education or less and 64 percent of those older than 45 are under
the age of 65.
These data were used in the development of the original 22-item meas-
ure and again here to develop and test the short form PAM (see Hibbard et al.
[2004] for details of the survey). To identify potential items for deletion, both
statistical and conceptual approaches were used. We relied on an iterative use
of Rasch analysis to identify items that could be eliminated without loss of
precision and reliability. Within each of the four stages of patient activation
items were identified that could be eliminated while still maintaining the
strong psychometric properties of the original measure. Candidate items were
deleted one at a time. With each item deletion the item scale locations were
recalibrated and the person reliability evaluated to check if and how much of a
decline in precision of measurement resulted from the deletion of the item.
When there was more than one item that was a good candidate for possible
deletion all candidate items in either that scale range or content domain were
tested and the item deletion resulting in the smallest decrease in precision of
person measurement was retained. Item fit values between 0.5 and 1.5 are
considered adequate (Smith 1996) and this was used as a benchmark in de-
cisions about item deletions. Item reduction was considered complete when
further deletions resulted in unacceptably low levels of reliability and preci-
sion. We had no predetermined number of items for the shorter version of
PAM. Once the item reduction was achieved we evaluated the performance of
the reduced PAM within various subgroups and compared the results to those
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1922 HSR: Health Services Research 40:6, Part I (December 2005)
of the 22-item PAM. Construct validity assessment on the PAM 13 was con-
ducted and compared with the PAM 22.
FINDINGS
The item reduction analysis resulted in a 13-item measure that has psycho-
metric properties similar to the original 22-item version. Figure 1 shows the
item scale calibrations of the PAM 13. The 13 items have a calibrated scale
range from 38.6 to 53.0 (on a theoretical 0–100 point scale), compared with
38.3–54.5 for the 22 items. Table 1 shows the item infit and outfit statistics for
the two versions. All of the infit and outfit statistics for the 13-item version of
the PAM fall well within the 0.5–1.5 acceptable range and are essentially the
same as in the 22-item version.
Table 2 shows the person reliability statistics for subgroups in the pop-
ulation when using the 13- and 22-item PAM. The 13-item version has slightly
Figure 1: Thirteen-Item Patient Activation Measure with Item Calibrations
and the Four Stages Identified
55 53
52
51
51
50
50
47
45
45 44 44
43
41 42
40 39
35
1 2 3 4 5 6 7 8 9 10 11 12 13
Four Stages of Activation 
Believes Active Role Important 
Confidence and Knowledge to Take Action 
Taking Action 
Staying the Course under Stress 
Key: Item calibrations are the calibrated scale value of the item. This represents how 
much activation is required to endorse the item. 
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Development and Testing of a Shorter Patient Activation Measure 1923
Table 1: Thirteen-Item PAM with Item Calibrations
Meas SEM Infit Outfit
1. When all is said and done, I am the person who is responsible 38.6 0.4 0.99 0.97
for managing my health condition
2. Taking an active role in my own health care is the most 41.1 0.4 1.01 0.93
important factor in determining my health and ability
to function
3. I am confident that I can take actions that will help prevent 41.5 0.4 0.93 0.85
or minimize some symptoms or problems associated with
my health condition
4. I know what each of my prescribed medications do 42.5 0.5 1.05 1.01
5. I am confident that I can tell when I need to go get medical 43.7 0.4 1.05 0.98
care and when I can handle a health problem myself
6. I am confident I can tell my health care provider concerns 43.8 0.4 0.92 0.86
I have even when he or she does not ask
7. I am confident that I can follow through on medical 45.3 0.4 0.94 0.87
treatments I need to do at home
8. I understand the nature and causes of my health condition(s) 47.0 0.4 1.01 0.95
9. I know the different medical treatment options available for 49.8 0.4 0.92 0.87
my health condition
10. I have been able to maintain the lifestyle changes for my 50.5 0.4 1.04 1.01
health that I have made
11. I know how to prevent further problems with my health 51.2 0.4 0.96 0.92
condition
12. I am confident I can figure out solutions when new situations 52.3 0.4 1.03 1.07
or problems arise with my health condition
13. I am confident that I can maintain lifestyle changes like diet 53.0 0.4 1.05 1.11
and exercise even during times of stress
Meas: The calibrated scale value of the item. This represents how much activation is required to
endorse the item.
SEM: The standard error of measurement in estimation of the item difficulty. SEM is the precision
of the item difficulty estimation and is shown in 0–100 units.
Infit: Infit mean square error is one of two quality control fit statistics assessing item dimensionality
(the degree to which the item falls on the same single, real number line as the rest of the items). Infit
is an information-weighted residual of observed responses from model expected responses and is
most sensitive to item fit when the item is located near the person’s scale location.
Outfit: Outfit mean square error fit statistic is most sensitive to item dimensionality when the item
scale location is distant from the person’s scale location.
PAM, Patient Activation Measure.
lower reliability for some subgroups: those with no chronic illness, those 85
years or older, those with self-rated poor health and those with lower income
and education. Thus, there is some loss of precision with the shorter version of
the PAM, however, these lower reliabilities still fall within an acceptable range.
Table 2 also shows the mean PAM-13 scores for different subgroups in
the population. As with the PAM 22 we see that those who are female,
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1924 HSR: Health Services Research 40:6, Part I (December 2005)
Table 2: Reliability of 13-Item PAM Compared with 22-Item PAM
Rasch Person Rasch Person
PAM 13 PAM 22
n % Pam13 Score Real Model Real Model
Sample 1,469 100 61.9 0.81 0.85 0.87 0.91
Gender: male 543 37 60.2 0.80 0.84 0.87 0.90
Female 926 63 62.8 0.82 0.85 0.89 0.91
Age group
45–54 512 38 63.9 0.82 0.85 0.88 0.91
55–64 398 28 61.7 0.81 0.85 0.88 0.91
65–74 290 20 61.9 0.80 0.84 0.89 0.91
75–84 185 13 58.2 0.78 0.83 0.87 0.90
85 or older 33 2 55.3 0.69 0.72 0.76 0.82
Self-rated health
Poor 102 7 54.3 0.73 0.78 0.83 0.87
Fair 236 16 57.3 0.78 0.83 0.84 0.88
Good 411 28 59.3 0.78 0.82 0.84 0.87
Very good 465 31 64.3 0.79 0.83 0.87 0.90
Excellent 255 18 68.7 0.83 0.85 0.89 0.91
Race
White 1,287 88 62.1 0.81 0.85 0.88 0.91
Black 111 8 58.6 0.78 0.82 0.84 0.88
Other 64 5 63.4 0.84 0.87 0.89 0.92
Education
High school or less 633 43 58.5 0.75 0.80 0.84 0.88
Some college 374 26 61.8 0.82 0.86 0.89 0.91
College graduate1 454 31 66.6 0.82 0.85 0.89 0.91
Household income
Less than $15,000 213 16 57.0 0.77 0.82 0.86 0.89
$15,000–$24,999 199 16 58.5 0.79 0.84 0.87 0.90
$25,000–$34,999 157 12 60.7 0.78 0.82 0.84 0.88
$35,000–$49,999 224 17 62.6 0.81 0.85 0.87 0.90
$50,000–$74,999 225 17 64.6 0.82 0.85 0.88 0.91
$75,000 or more 272 21 65.8 0.80 0.84 0.88 0.90
Chronic condition
None 311 21 63.1 0.78 0.82 0.87 0.90
Angina/heart problem 186 13 58.0 0.82 0.86 0.88 0.90
Arthritis 561 38 60.4 0.82 0.85 0.89 0.91
Chronic pain 362 25 59.1 0.83 0.86 0.88 0.91
Depression 212 15 58.5 0.81 0.85 0.87 0.89
Diabetes 170 11 59.7 0.79 0.83 0.88 0.91
Hypertension 498 34 59.7 0.80 0.84 0.88 0.91
Lung disease 177 12 60.1 0.83 0.86 0.87 0.91
Cancer 79 5 60.1 0.81 0.85 0.89 0.91
High cholesterol 445 30 60.9 0.81 0.85 0.89 0.91
Real person reliability is calculated under the assumption that all of the misfit in the responses is
because of departure of the data from the model’s expectations. Model person reliability is based
on the assumption that the data fit model expectations and that the misfit in the data is because of
the probabilistic nature of the model.
PAM, Patient Activation Measure.
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Development and Testing of a Shorter Patient Activation Measure 1925
younger, have more education, and better self-reported health have signifi-
cantly higher PAM scores ( po.001). Race is also significantly associated with
PAM score ( po.05).
When the 13-item PAM score is regressed on the 22-item PAM score it
accounts for 92 percent of the variation in the 22-item version estimated ac-
tivation. This verifies what would be expected from the comparative reliability
estimates; minimal information was lost in the item reduction process.
Finally, to assess the construct validity of the 13-item PAM, variables that
have been conceptually and empirically linked with the 22-item PAM are
examined for their relationship to activation as it is measured in the 13-item
PAM. Table 3 shows that the preventive behaviors, the disease-specific self-
management behaviors, and the consumeristic behaviors are all strongly
linked with activation scores using the 13-item PAM and that there is little
difference in these relationships regardless of whether the short or the long
form of PAM is used.
DISCUSSION
The results indicate that the shortened 13-item version is both reliable and valid.
The shorter version of the PAM will make it more feasible to use activation
scores to inform patient care plans. However, for PAM users who are seeking the
highest level of measurement precision, the PAM 22 may be more desirable.
PAM scores can provide insight into possible strategies for supporting
activation among patients at different points along the continuum. Patients
who score at the bottom of the measure may still believe that the doctor will
‘‘fix’’ them. Patients whose scores are somewhat higher, but are still in the
bottom half may understand that they must be involved in their care, but still
lack the basic knowledge about their conditions and their treatments that is
necessary for them to effectively act. Thus, patients scoring in the bottom half
of the measure likely need to work on self-awareness of their role in the care
process and in gaining the basic knowledge about their conditions.
Patient’s whose scores are in the upper half are beginning to gain con-
fidence in their ability to take on self-management behaviors and make life-
style changes. At this stage experiencing a series of small successes will likely
build a sense of self-efficacy and increase activation (Battersby et al. 2003).
Patients scoring near the upper range of the measure are likely to have made
changes in their lifestyles but may still have difficulty maintaining them when
new situations arise or when they are under stress. Thus, for those patients
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1926 HSR: Health Services Research 40:6, Part I (December 2005)
Table 3: Association between PAM Score and Construct Validity Indicators
Variable Mean Score Mean Score
Answer Category (n) on PAM 13 F; df p on PAM 22 F; df p
General preventive behaviors
Follow a low fat diet
Always or almost always (775) 64.2 59.1; 1, 1466 .001 60.5 72.1; 1, 1466 .001
Sometimes or never (693) 59.2 55.9
Follow regular exercise schedule
Yes (855) 64.2 74.4; 1, 1465 .001 60.7 122.1; 1, 1465 .001
No (612) 58.5 55.0
Five servings of fruits or vegetables per day
At least 4 days per week (730) 64.2 51.2; 1, 1466 .001 60.6 65.9; 1, 1466 .001
3 days per week or less (738) 59.5 56.1
Smoke tobacco
Yes (250) 59.9 6.7; 1, 1466 .010 56.0 14.4; 1, 1466 .001
No (1,218) 62.2 58.8
Disease specific behaviors
Diabetes
Use glucose journal
Always or almost always (108) 61.0 3.1; 1, 155 .079 57.6 4.5; 1, 155 .05
Sometimes or never (49) 57.3 53.8
Arthritis
Arthritis exercise
Always or almost always (249) 64.5 48.3; 1, 557 .001 60.4 62.7; 1, 557 .001
Sometimes or never (310) 57.2 53.7
High cholesterol
Follow a low fat diet
Always or almost always (249) 64.1 37.7; 1, 433 .001 60.1 40.1; 1, 433 .001
Sometimes or never (196) 56.9 53.9
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Development and Testing of a Shorter Patient Activation Measure 1927
Consumeristic behaviors
Before I go to a new health care provider,
I find out as much as I can about
his or her qualifications
Disagree or strongly disagree (232) 56.6 166.4; 2, 1374 .001 54.0 162.3; 2, 1374 .001
Agree (880) 59.6 56.4
Strongly agree (307) 72.8 67.4
When I do not understand, I am persistent
in asking my health care provider to
explain something until I understand it
Disagree or strongly disagree (79) 51.9 340.7; 2, 1447 .001 50.1 300.8; 2, 1447 .001
Agree (992) 58.0 55.3
Strongly agree (423) 73.6 67.7
PAM, Patient Activation Measure.
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1928 HSR: Health Services Research 40:6, Part I (December 2005)
scoring in the upper half of the PAM, working on developing a sense of self-
efficacy for taking on and maintaining behaviors is paramount.
Attaining the basic knowledge and beliefs reflected in early stages of
activation are likely necessary for building a sense of efficacy for the self-
management tasks involved in the later stages. We hypothesize that patients
need to sequentially pass through each of these stages on the way to becoming
effective self-managers. These stages have some similarities with the stages of
change in the Transtheoretical Model (Prochaska and DiClemente 1983;
Prochaska, Redding, and Evers 1997), which includes precontemplation,
contemplation, preparation, action, and maintenance stages. The Transthe-
oretical Model emphasizes motivation and readiness and does not explicitly
deal with issues of skill and knowledge acquisition. Further the Transtheoret-
ical Model focuses on one behavior at a time and requires the development of
a measurement tool specific to that behavior. The idea of tailoring interven-
tions to the patient’s stage is similar for both models.
While the PAM has strong psychometric properties, research is still
needed to make it fully ready for use in different settings and with different
populations. PAM users are beginning to translate the measure into other
languages. The degree to which the measure is valid and reliable in these
different language translations and among different cultures is unknown and
deserves investigation. While early evidence indicates the measure is valid and
reliable for different chronic illnesses, this too requires further study. Repli-
cation studies of the measure with different populations in different settings are
underway and will add to our understanding of these questions.
Research, which tests interventions that are effective in encouraging and
supporting patient advancement through the stages, is a high priority. It is very
likely that a strategy that will help a patient move from stage one (believing the
patient has an active role) to stage two (having the confidence and knowledge
to take action) is different from what will help her move into stage 3 (taking
action). That is, once a patient score or stage is known, what interventions are
efficacious in increasing that patient’s activation?
In addition to using the PAM score to inform interactions with patients,
an alternative approach has been tried in pilot efforts. Because the items can
be ordered by difficulty, it is possible to visually scan patient responses and
observe when their answers begin to move away from ‘‘strongly agree.’’ Cli-
nicians can use this as an opportunity to begin a conversation with the patient
about the item where responses changed. For example, ‘‘I see you are less sure
about your medications, let’s talk about that.’’ Using the PAM in this way can
sharpen the specificity of the interaction with the patient, increasing the prob-
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Development and Testing of a Shorter Patient Activation Measure 1929
ability that individual barriers and issues can be identified and dealt with. It
may be that using both the visual scan and the PAM score or stage may be the
most effective use of the measure. If this were the case, it might be advan-
tageous to use the full 22-item PAM to allow for more opportunities to identify
problems specific to an individual patient. Just using the ‘‘visual scan’’ ap-
proach is the easiest way to use the measure in a clinical encounter, partic-
ularly when electronic data collection is not an option. Because there is no
scoring involved and no data entry, testing the efficacy of this ‘‘low tech’’
approach is also a priority.
Among the interventions that do increase activation, what effect do they
have on patient health outcomes and costs?
Research on the use of the PAM for managing enrolled patient pop-
ulations is also needed. Would early intervention with patients identified
through screening as having both clinical risk factors and low skills (low PAM
scores), reduce costs and improve health outcomes?
Using the PAM as a basis for designing care plans and for assessing
individual and patient population progress appears to be a viable approach,
and one that warrants controlled testing to determine whether patients’ whose
care plans are informed by PAM scores have better outcomes and require less
health care resources than those patients whose care plans are not so informed.
ACKNOWLEDGMENT
Support for this study was provided by the Robert Wood Johnson Foundation
through grants #045524 and #050787.
NOTE
1. Because they responded to the PAM items in a way that suggest that they were
doing so in a rote or insincere manner (such as giving the same response to all
items), 46 of the 1,515 respondents were eliminated from the analysis.
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