Hindawi Publishing Corporation
Evidence-Based Complementary and Alternative Medicine
Volume 2012, Article ID 798098, 8 pages
DevelopmentandValidation of anInstrumentfor
MeasuringAttitudes and Beliefsabout Complementaryand
Alternative Medicine(CAM) Useamong CancerPatients
Jun J. Mao,1,2,3Steve C.Palmer,3,4KrupaliDesai,1SusanQ. Li,1
1Department of Family Medicine and Community Health, University of Pennsylvania Health System, Philadelphia, PA 19104, USA
2Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Health System, Philadelphia, PA 19104, USA
3Abramson Cancer Center, University of Pennsylvania Health System, Philadelphia, PA 19104, USA
4Department of Psychiatry, University of Pennsylvania Health System, Philadelphia, PA 19104, USA
5Department of Medicine, University of Pennsylvania Health System, Philadelphia, PA 19104, USA
Correspondence should be addressed to Jun J. Mao, email@example.com
Received 31 December 2011; Revised 25 March 2012; Accepted 27 March 2012
Academic Editor: Beverley de Valois
Copyright © 2012 Jun J. Mao et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Despite cancer patients’ extensive use of complementary and alternative medicine (CAM), validated instruments to measure
about CAM (ABCAM). The 15-item instrument was developed using the theory of planned behavior (TPB) as a framework. The
was then administered to 317 outpatient oncology patients. The ABCAM was best represented as a 3-factor structure: expected
benefits, perceived barriers, and subjective norms related to CAM use by cancer patients. These domains had Eigenvalues of 4.79,
2.37, and 1.43, and together explained over 57.2% of the variance. The 4-item expected benefits, 7-item perceived barriers, and
4-item subjective norms domain scores, each had an acceptable internal consistency (Cronbach’s alpha) of 0.91, 0.76, and 0.75,
respectively. As expected, CAM users had higher expected benefits, lower perceived barriers, and more positive subjective norms
(all P < 0.001) than those who did not use CAM. Our study provides the initial evidence that the ABCAM instrument produced
reliable and valid scores that measured attitudes and beliefs related to CAM use among cancer patients.
The use of complementary and alternative medicine (CAM)
is extensive among cancer patients [1–3]. Many cancer
patients turn to CAM therapies in addition to their con-
ventional treatments to deal with ongoing health issues
and increased symptom burden such as recurring pain and
psychological distress [4–6]. Population-based studies have
demonstrated that cancer patients are more likely to use
CAM than the general population [7, 8]; thus, it is important
to understand the attitudes and beliefs related to CAM use
among cancer patients in order to create a more personalized
integrative health system to tailor therapies to individual
beliefs and decision factors .
Why individuals use CAM is complex, personal, and
driven by multiple factors. Sociodemographic factors such
as female sex, younger age, higher education and income,
and white race have been associated with CAM use in
epidemiology studies [1, 10–17]. Research has also found
that individuals use CAM to improve their physical and
emotional health, enhance quality of life, strengthen the
immune system, minimize the side effects of conventional
medical treatments, and exert positive effects on cancer
[11, 12, 18–22]. Other psychological or culture factors relate
to CAM use may include being open to new experiences,
preferring natural/holistic approaches to treatment, and the
desire to exert a sense of personal control over their illness
[13, 20, 23, 24].
2Evidence-Based Complementary and Alternative Medicine
Several qualitative studies have provided unique insight
into the decision making process utilized by individuals
regarding CAM use. Verhoef and White interviewed 31
individuals who used CAM instead of conventional cancer
treatments and found several themes related to this decision
making process: the family’s/friend’s experiences with con-
care and physician communication, and patients’ beliefs and
need for control . Balneaves et al. interviewed breast
cancer patients about CAM use and developed the “bridging
the gap” model in categorizing individuals into three distinct
decision making style: taking one step at a time, playing
it safe, and bringing it all together . These kinds of
distinctive pathways have also been explored by Caspi et al.
Despite this emerging data, few studies have used
a theory-driven, well-developed instrument to guide the
inquiry into why individuals use CAM, especially in cancer
patients. In a recent systematic review of the research using
theoretical models to understand why individuals use CAM,
Lorenc et al. found that only 22 studies used a theoretical
model to predict CAM use, the majority of which were
among noncancer populations . The most commonly
used model was the health care utilization model, Andersen’s
sociobehavioral model [28, 29]. Existing research based
on this model predominantly evaluates social demographic
factors and symptoms without incorporating a compre-
hensive assessment of facilitators, barriers, and behavioral
predictors of CAM use [30–33]. Only one study focused
on understanding the psychological and behavioral factors
influencing the use of CAM in cancer patients; however, the
study was conducted in Japan, limiting the generalizability of
the study findings to other nations .
To further understand why cancer patients use CAM,
we can view CAM use as a set of health behaviors. Doing
so, we can draw upon the years of rigorous research in
health behaviors to understand the beliefs and attitudes
underlying CAM use. In particular, research has shown that
applying a theoretical model will both increase the ability
to predict health behaviors as well as lead to development
of interventions to change behaviors . A critical step
in beginning to incorporate health behavior methodology
in CAM research is the development and validation of
an instrument that can measure the attitudes and beliefs
predictive of CAM use among cancer patients.
We chose the theory of planned behavior (TPB)  as
a conceptual framework to guide the development of the
instrument. TBP posits that intentions to use CAM are an
important precursor of health behaviors and are influenced
by factors such as attitudes, subjective norms, and perceived
behavioral control. The TBP has been applied in hundreds of
health behavior and health service research studies and has
been found to be predictive of health behaviors as well as to
also chose the TPB because it is conceptually simple and may
help point out the major constructs that influence CAM use;
as such, it can serve as a starting point for further research
and inform intervention development to affect appropriate
integration of CAM into cancer care.
Thus, this study aims to develop and validate attitudes
and beliefs about complementary and alternative medicine
(ABCAM), an instrument capable of reliably measuring the
behavioral predictors of CAM use among cancer patients.
We hypothesize the factor structure of the instrument to be
consistent with that of the TPB domains and that the score
of the instrument will be reliable and valid.
2.1. Instrument Development. We developed the items for
this instrument through a systematic and critical review of
the existing literature on decision making about CAM use
in cancer and in the general population to identify relevant
conceptual models, instruments, and concepts. Additional
items were informed basing on qualitative interviews and
modified-grounded theory analysis conducted among 25
breast cancer survivors between 2008 and 2010 . The
qualitative interviews were conducted using TPB as a theo-
retical framework. Informed by our qualitative research and
literature search, initial instrument items were drafted to
evaluate the specific behavioral predictions of CAM use.
The initial items were reviewed by members (N
27) of the Penn Integrative Oncology Working Group for
face validity (March 2010). These members consisted of
physicians, nurses/nurse practitioners, psychosocial support
staff (e.g., psychologists, social workers, and nutritionists),
CAM practitioners (e.g., massage therapists, Reiki practi-
tioners, acupuncturists, and yoga instructors), and patient
tionnaire were revised based on feedback from the content
experts and stakeholders. Next, cognitive interviews were
conducted among patients with different types of cancer.
Participants were encouraged to share their thoughts about
the items with the researcher as they responded to them to
content, clarity, and burden. Items were then revised again
in discussion with key collaborators (JM, KD, and KA).
The initial scale consisted of 25 items (see appendix). Items
assessed agreement with statements concerning perceived
benefits, barriers, and subjective norms surrounding CAM
use on a 5-point Likert scale (“strongly disagree” to “strongly
2.2. Instrument Validation. We administered
among a convenience sample at three oncology practices
of the Abramson Cancer Center of the University of
Pennsylvania Health System (Philadelphia, PA, USA)
between May and August 2010. Eligible participants were
patients aged 18 or older who had a primary diagnosis of
cancer and a Karnofsky performance status of 60 or greater
(i.e., ambulatory). Additional inclusion criteria included
the approval of the patient’s oncologist and the patient’s
ability to understand and provide informed consent in
English. Trained research assistants screened medical records
and approached potential study subjects in the waiting
area of the oncology clinics. After discussing any concerns,
and signing the informed consent, each participant was
Evidence-Based Complementary and Alternative Medicine3
given a self-report survey. The study was approved by the
Institutional ReviewBoard of the University of Pennsylvania.
To assess criterion validity, we used the CAM Beliefs
health consumers. The CAMBI is a 15-item scale measuring
three aspects of CAM-related treatment beliefs: belief in
natural treatment, belief in participation in treatment, and
belief in holistic health . A higher score on the CAMBI
indicates more positive beliefs about CAM treatments.
The subscales had evidence of satisfactory reliability and
correlated with CAM use . While the CAMBI was not
validated in cancer populations, the constructs have some
degree of face validity to cancer patients. We hypothesized
that those who hold more holistic views about their health
would be likely to have greater expected benefit from CAM
therapies; thus offering some evidence of criterion validity.
To measure CAM use, we modified questions from the
National Health Interview Survey (NHIS) by asking individ-
uals: “have you used other sources of support or treatment
since your cancer diagnosis?” Response options included
common CAM items such as natural products (herbs),
megavitamins, relaxation techniques (deep breathing and
meditation), massage, chiropractic care, acupuncture, yoga,
qi gong, and tai chi  as well as therapies commonly used
in cancer patient populations such as expressive art therapies
and energy therapy . Patients answered each option with
a dichotomous response (yes; no). We previously used a
similar measure in several survey studies and generated the
prevalence of CAM use data reflecting that of the national
data [41, 42]. Although commonly reported by patients,
prayer was not included because findings suggest that factors
associated with its use are substantially different from use
of nonprayer CAM . Use of any type of CAM was then
dichotomized (yes; no).
2.3. Analyses. We first performed descriptive analyses to
examine missing data and item distribution. We performed
a series of principal component factor (PCF) analyses and
item reductions to identify the core factor structure of the
instrument. The PCF analysis was used because the primary
purpose was to identify and compute composite scores for
the factors underlying ABCAM. The number of factors was
determined by examination of Eigenvalues ≥1.00 and Scree
plot [43, 44]. We removed items that cross-loaded greater
than 0.3 and retained items that had a loading of 0.5 or
greater on the primary factor in an iterative process [45, 46].
Final Varimax-rotated loadings for individual items ranged
from 0.5 to 0.9. Oblique rotation was chosen to simplify
interpretation of factors, but summation scores rather than
factor scores were ultimately examined to avoid overfitting.
Cronbach’s alpha statistics were calculated to determine the
internal consistency of the scale. Coefficients of 0.70 or
greater are considered to be acceptable for an instrument
developed to evaluate differences in group means . To
evaluate construct validity, we used the Student’s t-test to
compare the scores in each domain between CAM users
and nonusers. We hypothesize that greater perceived benefit,
lesser perceived barriers, and perceived positive subjective
norms are associated with CAM use behaviors. To investigate
criterion validity, we correlated ABCAM subscales with the
CAMBI . It was expected that perceived benefits and
social norms would be positively correlated to domains of
CAMBI and that perceived barriers would be negatively
correlated to the domains in CAMBI. Data analysis was per-
formed using SPSS 19.0 for Windows (IBM SPSS Statistics
19.0). All statistical tests were two-sided with P < 0.05
indicating significance. We chose a sample size of at least
300 to allow adequate power to estimate reliability of the
Among the 317 participants (83% response rate), the mean
age was 58.4 with a standard deviation (SD) of 12.1; 244
(77.2%) were Caucasian; 56 (17.7%) were African American;
7 (2.2%) were Asian; 6 (1.9%) were Hispanic; 3 (0.9%)
identified themselves as other. While 88 (27.9%) reported an
education status of high school or less, 79 (25.1%) had some
graduate or professional education. Overall, 103 (32.5%) of
with breast cancer, 79 (24.9%) with gastrointestinal cancer,
and 47 (14.8%) with another type of cancer.
3.1. Factor Analysis. Of the 25 items included in the initial
instrument, one item, “reduce stress,” had missing data
greater than 5% and was excluded from analysis. The
remaining 24 items had missing data ranging from 1.5% to
4.4% with no apparent ceiling or flooring effects. Through
iterative factor analysis, we removed items that cross-loaded
to multiple domains as well as items that had low correlation
immune system,” “my family encourages me to use CAM,”
and “my friend asks me to try CAM” cross-loaded to both
expected benefits and social norms. Our final scale consisted
of 15 items with a 3-factor structure: expected benefits,
perceived barriers, and subjective norms (see Table 1). These
three domains had Eigenvalues of 4.79, 2.37, and 1.43, and,
together, explained over 57.2% of the variance in items.
The component scores were then calculated by summing the
individual items and normalizing to a value between 0 and
100 for each of the domains (see Table 2 and Figure 1 for
distribution of domain scores).
3.2. Reliability. The 4-item expected benefits, 7-item per-
ceived barriers, and 4-item subjective norms domain scales
each had an acceptable internal consistency (Cronbach’s
alpha coefficient) of 0.91, 0.76, and 0.75, respectively,
3.3. Construct Validity. Among the participants, 192 (60.6%)
of participants had used at least one type of CAM therapy
since cancer diagnosis. The most common approaches were
vitamin supplements (120, 34.0%), relaxation techniques
(77, 24.4%), herbs (75, 23.8%), special diet (64, 20.5%),
and massage therapy (55, 17.4%). As hypothesized, CAM
users had higher expected benefits (65.2 versus 52.1, t =
−5.79, P < 0.001), lower perceived barriers (43.9 versus 50.7,
4 Evidence-Based Complementary and Alternative Medicine
Table 1: Factor loadings and communalities based on a principal components analysis∗.
.20 I expect using CAM will decrease my emotional distress
I expect using CAM will reduce symptoms such as pain or fatigue related to
cancer and its treatment
I expect using CAM will prevent future development of health problems
I expect using CAM will help me cope with the experience of having cancer
I am unlikely or hesitant about using CAM because it may interfere with
the conventional cancer treatment
I am unlikely or hesitant about using CAM because treatments may have
I am unlikely or hesitant about using CAM because treatments cost too
I am unlikely or hesitant about using CAM because it is hard to find good
I am unlikely or hesitant about using CAM because I do not have time to
go to CAM treatments
I am unlikely or hesitant about using CAM because I do not have
knowledge about CAM treatments
I am unlikely or hesitant about using CAM because I do not have
transportation to CAM treatments
My health care providers (e.g., doctors, nurses, etc.) encourage me to use
My health care providers (e.g., doctors, nurses, etc.) are open to my use of
Other cancer patients think I should use CAM
My online support group encourages me to try CAM
Extraction method: principal component analysis.
Rotation method: Varimax with Kaiser normalization.
∗Rotation converged in 5 iterations.
Table 2: Descriptive statistics for the ABCAM sub-scales.
M (SD) Skewness Kurtosis Cronbach’s α
t = 3.62, P < 0.001), and more positive subjective norms
(52.3versus45.2,t = −4.96,P < 0.001)associatedwithCAM
than those who did not use CAM (see Figure 2).
of the ABCAM scale’s criterion validity, Pearson’s corre-
lations were calculated between ABCAM scale scores and
CAMBI scores (see Table 3). The expected benefit score
was positively correlated to both preference for natural
therapies and a holistic view of health. The perceived barrier
score was negatively correlated to belief in participation in
treatment decision and holistic health. The positive social
norm score was also positively correlated to belief in holistic
health. Interestingly, correlations between domain scores in
ABCAM and CAMBI were small-to-moderate suggesting
that our instrument is measuring different constructs from
This study sought to develop and validate the ABCAM
instrument to measure the decision factors related to the
use of CAM among cancer patients. The conceptual model
of ABCAM was guided by TPB. It was developed through
the literature review, qualitative research, expert review, pilot
testing, and quantitative psychometric analysis. The final
instrument consists of 15 items measuring three domains
related to the attitudes and beliefs predictive of CAM
use: expected benefits, perceived barriers, and subjective
norms. The scores appear to be reliable and valid in our
study population. As hypothesized, CAM users reported
higher expected benefits, lower perceived barriers, and more
positive subjective norms associated with CAM than those
who did not use CAM.
In comparison to existing questionnaires [13, 34, 49–51],
the ABCAM is the only one we know that has gone through
Evidence-Based Complementary and Alternative Medicine5
Expected benefits score
Perceived barriers score
0 20406080 100
Social norms score
Figure 1: Distribution of domain scores of the ABCAM.
the process from development to validation in cancer
patients. The theoretical model and content of our scale had
instrument improves upon. Additionally, all three domains
ceived barriers, and subjective norms, demonstrated higher
internal consistency than those reported by Hirai et al. .
were associated with CAM use among cancer patients.
Previous research has shown that cancer patients often use
CAM because perceiving it will improve their physical and
emotional health, enhance their quality of life, strengthen
their immune system, reduce symptoms, and have a positive
effect on cancer [10–12, 19–21]. Perceived positive outcomes
of CAM use were associated with higher CAM use among a
sample of Japanese cancer patients in a prior study . It
is important to note that while immune enhancement was a
response endorsed by participants, this item cross-loaded to
socialnorm whichdid not getretained in our finalshortened
instrument because it did not contribute to the unique factor
structure of the instrument. This further suggests the belief
that CAM improving one’s immune system appears to be
The literature suggests that some of the barriers toward
the use of CAM include lack of knowledge, perceived
ineffectiveness, cost, time constraint, access to the provider,
and perceived side effects of CAM therapies [10, 18, 52–54].
As expected, our study showed that cancer patients who used
CAM demonstrated lower perceived barriers as compared to
the construct of perceived behavioral control in the TPB.
It is important to note that some of barriers listed are
experienced by individuals but they are probably structural
barriers (e.g., cost, and access) as well. Therefore, these
barriers may be beyond the control of many individuals and
will require policy change, insurance coverage, and design
of an integrative health care delivery system to ultimately
Prior studies found that CAM users were more likely to
be of female sex, younger age, higher socioeconomic status
6 Evidence-Based Complementary and Alternative Medicine
Non CAM user
Figure 2: ABCAM domain scores by CAM users versus non-CAM
barrier domain may help understand what specific barriers
are experienced among different sociodemographic groups.
As evidence accumulates regarding the potential efficacy of
CAM integration. Using our instrument may help quantify
the level and significance of these barriers and to guide
interventions to target them.
Subjective norms play an important role in patients’
to use CAM if it is recommended by their family/friends
and/or their health care providers [34, 52]. Our study
revealed that CAM users had more positive subjective norms
than non-CAM users. This suggests that social approval
or disapproval may play an important role in influencing
patients’ use of CAM therapies; however, our items of fam-
ily/friend influence cross-loaded between expected benefits
and social norm and thus were removed from the final
instrument. Consistent with prior qualitative research [25,
55], our data further strengthens the evidence that fam-
efit of CAM use; thus, its social normative effect cannot be
separated from patients’ expected benefits derived from the
therapy. Another possible explanation is that cancer patients
often consider the opinion of their treating specialist as most
important and follow their advice [56–59]. As our instru-
ment is investigated in future research, we can tease out how
sources of social influence may shape expectations of thera-
The limitations to this study need to be acknowledged.
First, our qualitative interviews were conducted with breast
cancer patients in the context of decision making about
acupuncture; the content of the instrument may not be
complete. However, our questionnaire items were also
among content experts and patients with other cancers dur-
ing cognitive interviews. Second, our instrument was guided
by TPB as a conceptual framework and well captured the
domains in TPB, but like any conceptual model, it may not
Table 3: Relationship between domains in ABCAM and CAMBI∗.
P < 0.001
P = 0.57
P = 0.077
P = 0.17
P = 0.002
P = 0.84
P < 0.001
P < 0.001
P < 0.001
fully capture other important constructs such as preferences
for natural therapies, holistic health view, and finding hope
[39, 40, 60]. Additionally, we created a brief instrument
that can be incorporated into future cancer epidemiology
and health service research; thus, the format of ABCAM is
not a traditional TPB instrument. Third, our CAM use was
based on self-report and may not capture all of the CAM
therapies used by individuals; however, 60.9% use is in the
range of what is reported in existing literature . Forth,
nonparticipation bias is always a concern in an epidemiology
study. Our 83% participation rate is acceptable in survey
research, but cannot rule out the potential for selection bias.
Lastly, our study was conducted in a large academic cancer
center, and future research, including community cancer
practices, is needed to increase the generalizability of this
In conclusion, this study provided the initial evidence
that the ABCAM produced a reliable and valid score for
measuring the behavioral predictors of CAM use. Future
research is needed to demonstrate additional aspects of
reliability and validity (e.g., confirmatory factor analysis;
test-retest reliability; sensitivity to change). In addition,
prospective research is needed to determine whether these
attitudes and beliefs—expected benefits, perceived barriers,
and subjective norms—predict both intended and actual use
of CAM among cancer patients. Ultimately, this instrument
will help elucidate how demographic, socioeconomic, and
cultural issues may relate to these attitudes and beliefs,
thereby influencing CAM use in the context of cancer care.
Such understanding is necessary to guide the appropriate
integration of CAM into the conventional health system to
improve the health and wellbeing of diverse populations of
See Supplementary Material available online at doi:10.1155/
The authors would like to thank all the cancer patients
and survivors, physicians, nurse practitioners, and staff for
their support. They would like to thank Eitan Frankel,
Neha Agawal, Tiffany Chen, and Jonathan Burgess for their
dedication to the data collection and management process.
J. J. Mao is supported by a K23 AT004112 Grant from
Evidence-Based Complementary and Alternative Medicine7
the National Center for Complementary and Alternative
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