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Relationship of Internet Health Information Use With Patient Behavior and
Self-Efficacy: Experiences of Newly Diagnosed Cancer Patients Who
Contact the National Cancer Institute's Cancer Information Service
Sarah Bauerle Bassa; Sheryl Burt Ruzeka; Thomas F. Gordona; Linda Fleisherb; Nancy McKeown-Connb;
Dirk Moorec
a
Temple University Department of Public Health, Philadelphia, Pennsylvania, USA b Atlantic Region
Cancer Information Service, Fox Chase Cancer Center, Cheltenham, Pennsylvania, USA c Department
of Biostatistics, University of Medicine and Dentistry of New Jersey, School of Public Health,
Piscataway, New Jersey, USA
To cite this Article Bass, Sarah Bauerle , Ruzek, Sheryl Burt , Gordon, Thomas F. , Fleisher, Linda , McKeown-Conn,
Nancy and Moore, Dirk(2006) 'Relationship of Internet Health Information Use With Patient Behavior and Self-Efficacy:
Experiences of Newly Diagnosed Cancer Patients Who Contact the National Cancer Institute's Cancer Information
Service', Journal of Health Communication, 11: 2, 219 — 236
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Journal of Health Communication, 11:219–236, 2006
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DOI: 10.1080/10810730500526794
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Relationship of Internet Health Information
Use With Patient Behavior and Self-Efficacy:
Experiences of Newly Diagnosed Cancer
Patients Who Contact the National Cancer
Institute’s Cancer Information Service
SARAH BAUERLE BASS, SHERYL BURT RUZEK,
AND THOMAS F. GORDON
Temple University Department of Public Health, Philadelphia,
Pennsylvania, USA
LINDA FLEISHER AND NANCY McKEOWN-CONN
Atlantic Region Cancer Information Service, Fox Chase
Cancer Center, Cheltenham, Pennsylvania, USA
DIRK MOORE
University of Medicine and Dentistry of New Jersey,
School of Public Health, Department of Biostatistics, Piscataway,
New Jersey, USA
This study examines the relationship of Internet health information use with patient
behavior and self-efficacy among 498 newly diagnosed cancer patients. Subjects
were classified by types of Internet use: direct use (used Internet health information
themselves), indirect use (used information accessed by friends or family), and
non-use (never accessing Internet information). Subjects were recruited from callers of the National Cancer Institute’s (NCI’s) Cancer Information Service, Atlantic
Region. They were classified by type of Internet use at enrollment and interviewed
by telephone after 8 weeks. There were significant relationships among Internet use
and key study variables: subject characteristics, patient task behavior, and selfefficacy. Subjects’ Internet use changed significantly from enrollment to 8 week
follow-up; 19% of nonusers and indirect users moved to a higher level of Internet
use. Significant relationships also were found among Internet use and perceived
patient–provider relationship, question asking, and treatment compliance. Finally,
Internet use was also significantly associated with self-efficacy variables (confidence
in actively participating in treatment decisions, asking physicians questions, and
This study was funded by the National Cancer Institute to Temple University, grant #1
RO3 CA90145-01, ‘‘Internet Use by Cancer Patients,’’ Sarah Bauerle Bass, PhD, MPH,
principal investigator. The authors express gratitude to Dr. Gary Kreps, Former Branch Chief
of the NCI Health Communications and Informatics Research Branch for support and guidance of this project.
Address correspondence to Sarah Bauerle Bass, PhD, MPH, Department of Public Health,
Temple University, 1700 N. Broad St., Suite 304, Philadelphia, PA 19122, USA. E-mail: sbass@
temple.edu
219
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S. B. Bass et al.
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sharing feelings of concern). The results of this study show that patients who are
newly diagnosed with cancer perceive the Internet as a powerful tool, both for
acquiring information and for enhancing confidence to make informed decisions.
Growth in technology has created a communications revolution that allows people
instant and equal access to previously unavailable or difficult-to-find information.
New media reportedly empower people to become active participants in their own
health care by giving them necessary information to make informed decisions, and
to engage in behaviors that will improve their health (National Cancer Institute
[NCI], 2000). The growth in the use of personal computers and the Internet is substantial, with an ever-widening audience adopting the technology and incorporating
it into their daily lives. Since 1998, the number of households with computers
increased from 42.1% to almost 62% in the latest United States Department of
Commerce (USCDC) report (2004). The most current estimates show that more than
201 million individuals are accessing the Internet, 68.6% of the U.S. population.
This represents a 111.5% increase in the last 4 years (Internet World Stats, 2004).
This explosion in use is transforming the access and delivery of health information. Consumers have access to the same medical information available to
doctors. This availability has the potential to significantly change the relationship
between patient and provider, alter the ways patients and providers communicate,
and help create a consumer base of power in health policy and decision making. The
number of people searching for Internet health information has increased dramatically,
from 7.8 million in 1996, to 23.3 million in 1999 (Miller & Reents, 1998), and to nearly
100 million in 2001 (Harris Interactive, 2001). Current surveys by the Pew Internet and
American Life Project (PIALP) show that 80% of adult Internet users report searching
for at least one of 16 major health topics on-line (Fox & Fallows, 2003).
E-patients say they feel empowered by having the information because it allows
them to ask their doctors well-informed questions. In the PIALP survey, 73% of
health seekers say the Internet has improved the health and medical information
and services they receive. In a Harris Interactive study, approximately 60% of users
agree that health information technology gives them a sense of control and empowerment in managing their health. Another 63% believe information technology will
save them from making unnecessary visits to the doctor (Harris Interactive, 2003).
Similarly, the Internet User Survey found 70% of health information retrievers
agreed that the Internet empowered them to make better choices in their lives—compared with 55% of nonretrievers. In addition, 47% of respondents said that the
information they found on the Internet affected the decisions they made about
health treatment or care. Almost half also say that the information has improved
the way they take care of themselves and 55% report that it improves the way they
get health information (Fox & Ranie, 2000).
That people are accessing Internet health information and acting on the information they find is not in question. Little is known empirically, however, about how
Internet use correlates with patient behavior characteristics, perceived self-efficacy, or
other psychosocial variables, especially when a person is diagnosed with a serious or
life threatening disease. Task behavior and self-efficacy, key concepts in Roter and
Hall’s (1997) Patient-Provider Communication Theory and Bandura’s (1995) SelfEfficacy Theory, are important to understand because these factors can influence
patient–provider relations. Roter and Hall have postulated that patient behavior
has a reciprocal relationship with provider behavior and that changing either will
Use of Internet Information by Cancer Patients
221
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have a direct effect on the other. Similarly, Bandura posits that a patient’s self-efficacy
can be influenced through very specific and targeted interventions. The question is
whether Internet health information use could influence a patient in these ways.
This study established baseline data on self-reported use of Internet health information by a group of patients newly diagnosed with cancer by assessing self-reported
Internet health information use among these patients to better understand the relationship among Internet use, patient task behavior in medical encounters, and perceived
self-efficacy. These patients were callers to the Atlantic Region office of the Cancer
Information Service (CIS), a service provided by the NCI (1-800-4-CANCER). To understand patient behavior among newly diagnosed cancer patients who were or were not
obtaining Internet health information, we asked the following questions:
1. What is the relationship between level of Internet information-seeking use and
information-seeker characteristics?
2. Does level of Internet use change from initial call to 8-week follow-up?
3. Are there differences in self-reported patient task behaviors during visits with
health care providers among different Internet use levels?
4. Are there differences in perceived self-efficacy among different Internet use levels?
Methods
The sample was designed to be a total census of all eligible patients who called the
NCI’s CIS (1-800-4-CANCER) telephone service at the Atlantic Region office, located
at Fox Chase Cancer Center in Philadelphia, Pennsylvania, in the study enrollment
period, from June 18, 2000, to February 28, 2002. The eligibility requirements included
the following: (1) being aged 18 years or older; (2) being newly diagnosed with cancer
(within 8 weeks of the call) and it not being a recurrence; and (3) not yet having begun
cancer treatment. The CIS cancer information specialists initially assessed eligibility
based on the content of the caller’s request for information.
Six hundred forty-eight callers contacted the CIS during the study enrollment
period and were qualified to participate. Of these, 498 (76.9%) agreed to participate,
and 150 (23.1%) declined. Among participants enrolled, 442 (88.8%) completed
both the baseline protocol and the follow-up survey. Drop-outs included 21
(4.1%) who declined to complete the follow-up survey, 4 (1%) who had died, and
31 (6.1%) who did not respond to phone or mail.
Each subject was mailed a letter 3 weeks after enrolling, confirming that they
would receive a follow-up interview within 1 month. Telephone interviews were conducted 8 weeks after enrollment to allow adequate time for newly diagnosed patients
to seek information and social support on the Internet and to negotiate treatment
with their doctors. It was expected that at eight weeks subjects would be able to
recall information needed to assess their patient task behavior and self-efficacy.
Participants were called until contacted, unless they were unreachable after 10
phone calls, or indicated that they no longer wished to participate in the study. Subjects who were unreachable after 10 phone calls were mailed a printed survey and
asked to complete and return it in a postage-paid envelope.
Instruments
Self-Efficacy. To measure perceived self-efficacy in cancer patients, or confidence in being able to accomplish a task, the Merluzzi Cancer Self-efficacy scale
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S. B. Bass et al.
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(Merluzzi, Nairn, Hegde, Martinez Sanchez, & Dunn, 2001) was used. This 14-item
scale measures confidence in accomplishing behaviors related to having cancer,
including items on maintaining independence, maintaining a positive attitude,
expressing negative feelings about cancer, and managing treatment side effects.
Patient Task Behavior. Since patient and provider task behavior is usually
assessed using audio or video tape analysis, no validated scale was available to test
perceived task behavior, such as the overt behavior a patient would exhibit during an
encounter with a provider, including asking questions, making lists of questions,
doing research prior to the appointment, recalling information, and complying with
recommended treatments. Therefore, a combination of questions was utilized and
adapted from three different scales, including the Ware scale (Ware & Hays, 1988)
on patient satisfaction, the Roter Interaction Analysis System (Roter, 1991), and the
Sutherland ‘‘Information Seeking Questionnaire’’ (Sutherland, Llewellyn-Thomas, &
Lockwood, 1989).
Subject Characteristics. A random sample of all presumptively eligible study
subjects who called the CIS were asked questions about their sociodemographic
characteristics. In addition, level of Internet use was assessed based on questions
developed for this study and pilot tested with 10 CIS callers. At baseline, a single
question was asked to classify subjects into one of three Internet user categories:
direct user, indirect user, or nonuser. Participants were asked,
‘‘Thinking about the past year, would you characterize yourself as someone who has never looked up health information on the Internet’ [nonuser], has received Internet health information from a friend or family
member but has not looked up the information yourself [indirect user],
or, has looked up health information on the Internet [direct user]?’’
Reasons for Internet Use. After analyzing interview data from the first 150 study
participants, questions were added to the follow-up interview about why participants
had changed their Internet use. A subset of subjects was asked, at follow-up, a series
of open-ended and yes–no questions about why they had changed their use of
Internet health information.
Interview Procedures
All interviews were conducted by CIS cancer information specialists who are trained
to handle cancer information calls and to follow research protocols. Callers who met
the three eligibility criteria described above were asked to participate in the study
and told that participation would include a follow-up call in 8 weeks. An oral
informed consent protocol was initiated and the same demographic data were collected as were collected for all persons who call the CIS. A single question was asked
to classify subjects by type of Internet use.
Follow-up phone interviews were made 8 weeks after initial contact to assess
impact of the use of the Internet on perceived patient task behavior and self-efficacy.
These calls also were made by trained CIS staff. At follow-up, the interview protocol
included questions to assess self-reported Internet use, additional subject characteristics, perceived patient behavior in patient–provider communication, and perceived
self-efficacy in coping with cancer.
Use of Internet Information by Cancer Patients
223
Results
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Subject Characteristics
The 442 subjects were predominately White (91.3%), between the ages of 51 and 70
years old (54.3%), and had completed high school or college (51.7%). Most were
residents of the three states served by the Atlantic Region CIS (Pennsylvania,
New Jersey, and Delaware). There were no statistically significant differences among
the sociodemographic characteristics of study participants, compared with all persons who called the CIS telephone information line.
There were five statistically significant characteristics that defined direct users,
compared with both indirect and nonusers (see Table 1). Specifically, age, gender,
income, employment status, and education level were all positively associated
with being a direct or indirect user. Direct users were more likely to be female
(v2 ¼ 16:68; p < :001), between the ages of 50 and 60 (v2 ¼ 51:68; p < :001),
employed (v2 ¼ 48:27; p < :001), a college graduate (v2 ¼ 54:94; p < :001), and earn
more than $60,000 a year (v2 ¼ 47:97; p < :001). This is consistent with other national
data on Internet user characteristics.
Levels of Internet Use and Internet Access
The same question about use of Internet health information was asked at enrollment
and again at follow-up to assess whether subjects had changed their Internet use during the 8 weeks following contact with the CIS. At baseline, 45.5% of all subjects
reported using the Internet directly (direct users), benefiting from others’ use
(indirect users), or reported not using the Internet to look up health information
(nonusers). Table 2 compares Internet use at baseline and at the 8-week follow-up.
A statistically significant number of subjects (18.7%) reported patterns of Internet
use that had changed from baseline (v2 ¼ 456:3; p < :001). As Table 1 indicates,
65 or 44.5% of those saying they had never looked up Internet health information
changed to either the indirect or direct user category. Analysis shows that 33
(22.6%) nonuser participants changed to the indirect category and 32 (21.9%) changed to the direct category, indicating that because they received a cancer diagnosis
they were more motivated to find health information. In addition, 18 people reporting they were indirect users at recruitment indicated they were direct users at followup (18.9%). None of the subjects reported a lower level of Internet use of health
information at 8 weeks, compared with baseline.
Reasons for Changing Type of Internet Use
A subset of 35 subjects was asked why they had changed their use of Internet health
information. First, they were asked an open-ended question: ‘‘I notice that when you
called the Cancer Information Service 6 weeks ago you characterized yourself as
someone who ‘had never looked up Internet health information’ or ‘had received
Internet health information from a friend or family member but had not looked
up the information yourself.’ Why do you think your behavior has changed since
that initial call?’’. Next, participants were asked a series of yes or no questions about
specific reasons for changing their use of the Internet: (1) ‘‘My diagnosis, and wanting to know more about my disease,’’ (2) ‘‘I had questions about treatment options,’’
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S. B. Bass et al.
Table 1. Significant demographic characteristics of study participants completing
follow-up survey by internet user category
Characteristic
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Gender
Male
Female
Education
Less than high
school
High school
graduate
Some college
College graduate
Some graduate
school or degree
Age
18–30
31–40
41–50
51–60
61–70
Over 70
Employment
Employed
Unemployed
Retired
Homemaker
On leave
Disabled
Other
Household income
$0–$15,000
$15,000–$30,000
$31,000–$60,000
Over $60,000
Refused
Total
Direct user
Indirect user
Nonuser
(N ¼ 441)
215 (48.8%)
226 (51.2%)
(N ¼ 251)
109 (43.4%)
142 (56.6%)
(N ¼ 109)
50 (45.9%)
59 (54.1%)
(N ¼ 81)
56 (69.1%)
25 (30.9%)
(N ¼ 441)
24 (5.5%)
(N ¼ 251)
7 (2.8%)
(N ¼ 109)
7 (6.4%)
(N ¼ 81)
10 (12.3%)
123 (27.9%)
45 (17.9%)
42 (38.5%)
36 (44.4%)
105 (23.8%)
95 (21.5%)
94 (21.3%)
63 (25.1%)
68 (27.1%)
68 (26.1%)
23 (21.1%)
18 (17.4%)
19 (29.0%)
19 (23.5%)
9 (11.1%)
7 (8.6%)
(N ¼ 440)
13 (3.0%)
36 (8.2%)
73 (16.6%)
120 (27.3%)
119 (27.0%)
79 (17.9%)
(N ¼ 441)
150 (34.0%)
15 (3.4%)
155 (35.1%)
35 (7.9%)
54 (12.2%)
29 (6.6%)
3 (.7%)
(N ¼ 251)
10 (4.0%)
36 (10.0%)
73 (22.7%)
120 (29.9%)
59 (23.5%)
25 (10.0%)
(N ¼ 251)
102 (40.6%)
9 (3.6%)
61 (24.3%)
20 (8.0%)
42 (16.7%)
15 (6.0%)
2 (.8%)
(N ¼ 109)
3 (2.8%)
8 (7.3%)
12 (11.0%)
27 (24.8%)
35 (32.1%)
24 (22.0%)
(N ¼ 109)
34 (31.2%)
2 (1.8%)
45 (41.3%)
11 (10.1%)
8 (7.3%)
8 (7.3%)
1 (.9%)
(N ¼ 80)
(N ¼ 442)
38 (8.6%)
80 (18.2%)
113 (25.7%)
167 (38.0%)
42 (9.5%)
(N ¼ 250)
13 (5.2%)
32 (12.8%)
59 (23.6%)
123 (49.2%)
23 (9.2%)
(N ¼ 109)
9 (8.3%)
26 (23.9%)
36 (33.0%)
25 (22.9%)
13 (11.9%)
3 (3.8%)
4 (5.0%)
18 (22.5%)
25 (31.3%)
30 (37.5%)
(N ¼ 81)
14 (17.3%)
4 (4.9%)
49 (60.5%)
4 (4.9%)
4 (4.9%)
6 (7.4%)
(N ¼ 81)
16 (19.8%)
22 (27.2%)
18 (22.2%)
19 (23.5%)
6 (7.4%)
p < .001.
Table 2. Type of Internet use at baseline and follow-up (N = 442)
Type of Internet user
Direct user
Indirect user
Nonuser
p < .001.
Baseline
Follow-up
201 (45.5%)
95 (21.5%)
146 (33.0%)
251 (56.8%)
110 (24.9%)
81 (18.3%)
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Use of Internet Information by Cancer Patients
225
(3) ‘‘I have a computer easily accessible,’’ (4) ‘‘I have a family member or friend who
offered to look up information,’’ (5) ‘‘I have a family member or friend who encouraged me to look up information,’’ (6) ‘‘I received information about websites from
the Cancer Information Service,’’ (7) ‘‘I received information about websites from
other organizations,’’ (8)‘‘My doctor recommended that I look up the information,’’
and (9) ‘‘My nurse or other health care provider recommended that I look up the
other information.’’ The interview protocol was designed so participants could
respond ‘‘yes’’ to more than one question, providing multiple reasons for changing
their use of Internet health information.
As shown in Table 3, the most common response to the open-ended question
was the ‘‘diagnosis’’ (40%). Presumably, respondents were prompted to seek out
Internet health information in new ways in response to having been diagnosed with
a serious, life-threatening condition. For 37%, family and friends urged them to find
information, and 8.6% cited computer availability and receiving training on how to
get Internet health information. Some (22.9%) reported that they ‘‘just never got
health information before now.’’ In response to the yes=no questions, subjects most
frequently cited their diagnosis (82.1%), having questions about treatment options
(87.2%), and having friends or family members offer to look information up for
them (87.2%) as reasons for their change in Internet use. Few participants said their
Table 3. Subjects’ reasons for changing use of internet from baseline at 8-week
follow-up (N ¼ 39)
Yes
Open-ended question (N ¼ 35)
Diagnosis
Friends=family encouraged
Got training
Other
Specific questions
Diagnosis (N ¼ 39)
Questions about diagnosis and treatment (N ¼ 39)
Computer accessibility (N ¼ 39)
Family=friend offered to look for information
(N ¼ 39)
Family=friend encouraged participant to look for
information (N ¼ 38)
CIS encouraged participant to look for information
(N ¼ 38)
Other organization encouraged participant to look
for information (N ¼ 38)
Doctor encouraged participant to look for
information (N ¼ 39)
Other HCW encouraged participant to look
for information (N ¼ 39)
No
14
13
3
8
(40.0%)
(37.1%)
(8.6%)
(22.9%)
21
22
32
27
(60.0%)
(62.9%)
(91.4)
(77.1%)
32
34
28
34
(82.1%)
(87.2%)
(71.8%)
(87.2%)
7
5
11
5
(17.9%)
(12.8%)
(28.2%)
(12.8%)
21 (55.3%)
17 (44.7%)
22 (58.0%)
16 (42.0%)
17 (44.7%)
21 (55.3%)
2 (5.1%)
37 (94.9%)
6 (15.4%)
33 (84.6%)
þNumber of respondents vary because of nonresponse to the open-ended question but willingness to answer yes=no questions.
226
S. B. Bass et al.
doctor (5.1%) or other health care worker (15.4%) encouraged them to look up
Internet health information.
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Level of Internet Use and Perceived Task Behavior
Patient task behavior was measured using a number of variables, including perceived
levels of participation with physician, perceived relationship with physician, question-asking behavior, information gathering prior to an appointment, recall of information, ability to carry out treatment recommendations, and overall satisfaction
with physician. All participants were asked to answer questions specifically thinking
about their last visit with their primary cancer doctor. There were statistically significant relationships among Internet use and perceived relationship with their doctor,
question-asking behavior, and treatment compliance.
Subjects were asked to describe their perceived relationship with their physician
by choosing a statement that best described the relationship: (a) physicians made all
decisions, (b) participants made all the decisions, or (c) participants had a supportive
partnership with their physician where both made decisions. Comparing subjects by
level of Internet use, we note that 74.1% of direct users, 77.0% of indirect users, and
56.8% of nonusers said they had supportive partnerships with their physicians. This
difference was statistically significant for both direct and indirect users, compared
with nonusers (v2 ¼ 14:01; p < :03). While only 11.6% of direct users and 9.2%
of indirect users said their doctors made all the decisions, almost 25% of nonusers
made this statement (see Table 4).
Question-asking behavior was assessed in two ways. First, participants were
asked if they had prepared a list of questions for the doctor prior to their last visit.
Those who answered ‘‘yes’’ also were asked if they had asked all the questions on
their list during their last doctor visit. Second, all participants were asked how many
questions they thought they had asked the doctor regarding their cancer or treatment
during their last visit.
Of those completing the follow-up survey, 73.1% indicated that they had prepared a list of questions for the doctor. Of those completing a list, 90.1% indicated
that they had asked the doctor all the questions on the list. Looking at list preparation by Internet user group, we found that 82.5% of direct users, 66.4% of
indirect users, and 53.1% of nonusers reported preparing a list. Analysis shows a
statistically significant difference; a greater proportion of direct users of Internet
Table 4. Subjects’ perceived relationship with doctor by type of internet group at
follow-up
Total sample Direct users Indirect users Nonusers
(N ¼ 440)
(N ¼ 250)
(N ¼ 109)
(N ¼ 81)
Relationship
Doctor makes all decisions 59 (13.4%) 29 (11.6%) 10 (9.2%) 20 (24.7%)
I make all Decisions
56 (12.7%) 31 (12.4%) 12 (11.0%) 13 (16.0%)
Partnership
315 (71.6%) 185 (74.0%) 84 (77.0%) 46 (56.8%)
Other
10 (2.3%)
5 (2.3%)
3 (2.8%)
2 (2.5%)
2
v ¼ 14:01; p < :05.
Use of Internet Information by Cancer Patients
227
Table 5. Subjects’ question asking behavior by type of internet use at follow-up
Total sample Direct users Indirect users
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Prepare list of questions?
Yes
No
p < .001;
p < .001;
(N ¼ 110)
73 (66.4%)
37 (33.6%)
(N ¼ 81)
43 (53.1%)
38 (46.9%)
(N ¼ 75)
(N ¼ 43)
291 (90.1%) 187 (91.2%)
32 (9.9%)
18 (8.8%)
(N ¼ 440)
(N ¼ 250)
65 (86.7%)
10 (13.3%)
(N ¼ 109)
39 (90.7%)
4 (9.3%)
(N ¼ 81)
19
11
89
137
184
5
3
34
30
37
9
5
19
27
21
(N ¼ 323)
If prepared list,
ask all questions?
Yes
No
#Questions asked
during last visit
None
1
2–3
4–5
6 or more
(N ¼ 442)
(N ¼ 251)
323 (73.1%) 207 (82.5%)
119 (26.9%) 44 (17.5%)
Nonusers
(N ¼ 205)
(4.3%)
5
(2.5%)
3
(20.2%) 36
(31.2%) 80
(41.8%) 126
(2.0%)
(1.2%)
(14.4%)
(32.0%)
(50.4%)
(4.6%)
(2.8%)
(31.2%)
(27.5%)
(33.9%)
(11.1%)
(6.2%)
(23.5%)
(33.3%)
(25.9%)
p < .001.
health information prepared lists of questions compared with either indirect or
nonusers (v2 ¼ 30:23; p < :001; see Table 5).
When queried about questions asked at their last visit, 41.8% of subjects indicated that they asked six or more questions; 31.2% reported asking 4 to 5 questions,
and 20.2% reported asking 2 to 3 questions. There is a similar pattern for each type
of Internet user. Six or more questions were reportedly asked by 50.4% of direct
users, 33.9% of indirect users, and 25.9% of nonusers. These differences were statistically significant (v2 ¼ 40:52; p < :001), with direct users reporting that they asked
more questions than indirect and nonusers (see Table 5).
Treatment compliance was measured by two questions. Subjects were asked if
during the last doctor’s visit any treatment or therapy recommendations (medications, cancer treatments, lifestyle changes, etc.) were made. Subjects who reported
that their physician had made recommendations were asked to rate how they carried
out those recommendations. Of the 295 subjects who said they had been given treatment or therapy recommendations by their physicians, 63.1% said they had carried
out all recommendations, 9.2% said they had carried out most recommendations,
13.2% said they had carried out some recommendations, and 14.6% said they did
not carry out any recommendations. Direct users were more likely to say that they
had carried out only some recommendations or did not carry out the recommendations at all, compared with indirect and nonusers, a difference that was statistically
significantly different (v2 ¼ 18:4; p < :05; see Table 6).
Type of Internet Use and Perceived Self-efficacy
Subjects were administered 14 self-efficacy measures that used a 9-point response
scale, with one meaning the person was not at all confident of being able to
accomplish the task, and 9 being totally confident of being able to accomplish the
228
S. B. Bass et al.
Table 6. Subjects’ treatment compliance behavior by type of internet use at follow-up
Rate compliance
Carried out all
recommendations
Carried out most
recommendations
Carried out some
recommendations
Carried out none
Total sample
Direct user
Indirect user
Nonuser
(N ¼ 295)
186 (63.1%)
(N ¼ 169)
100 (59.2%)
(N ¼ 68)
44 (64.7%)
(N ¼ 58)
42 (72.4%)
27 (9.2%)
13 (7.7%)
10 (14.7%)
4 (6.9%)
39 (13.2%)
32 (18.9%)
2 (2.9%)
5 (8.6%)
43 (14.6%)
24 (14.2%)
12 (17.6%)
7 (12.1%)
2
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v ¼ 18:4; p < :05:
task. Participants were instructed to rate each of the patient tasks by their confidence
in being able to do the task, whether or not they had actually done it in the past.
Overall ANOVA analysis indicated that 3 of the 14 self-efficacy measures were
significant in the sample (actively participating in treatment decisions, asking physicians questions, and sharing feelings of concern; see Table 7). To analyze whether
Table 7. ANOVA results of self-efficacy measures: participating in treatment
decisions, asking physicians questions, and sharing feelings of concern by type of
internet use at follow-up (N ¼ 439)
Direct users Indirect users Nonusers
(A)
(B)
(C)
Dependant variable
Actively participating in
treatment decisions
Mean
SD
N
Significant differences with
other groups
Asking physicians questions
Mean
SD
N
Significant differences with
other groups
Sharing feelings of concern
Mean
SD
N
Significant differences
with other groups
p < .05;
p < .005;
p < .001.
F
df
438
8.16
1.38
251
>C
8.19
1.44
109
>C
7.52
2.34
81
<A,B
5.36
8.54
1.00
251
>C
8.49
1.14
109
>C
7.80
2.03
81
<A, B
5.13 438
7.34
1.82
250
>C
7.56
1.74
108
>C
6.74
2.60
81
<A, B
4.19
436
Use of Internet Information by Cancer Patients
229
Table 8. Step down regression analysis of significant self-efficacy variables controlling
for demographic variables (N ¼ 436)
Variable
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Actively participating in treatment decisions
Asking physicians questions
Sharing feelings of concern
Odds ratio
95% CI
P-value
1.26
1.38
1.17
1.08–1.47
1.15–1.64
1.02–1.35
0.002
0.000
0.013
self-efficacy differed among and between direct, indirect, and nonusers of Internet
health information when controlling for demographic characteristics, a stepwise
logistic regression equation was constructed so that significant subject characteristics
(gender, age, income, education level, and severity of disease) were introduced to the
equation to adjust for their effects on the overall results. The dependent variable
(Internet use) was coded with direct and indirect users as ‘‘1’’ and nonuser as ‘‘0’’;
thus significant odds ratios indicated an increased likelihood of being a direct or
indirect user. Direct and indirect users were grouped together because ANOVA
analysis indicated they were not significantly different from each other, but both
were different from nonusers.
When these variables were entered into the model, the same three measures
(actively participating in treatment decisions, asking physicians questions, and sharing feelings of concern) were statistically significant (p < :002; p < :001; p < :02,
respectively; see Table 8). This finding indicates that the relationship between these
variables and type of Internet use is a true relationship after controlling for age, gender, income, educational attainment, type of cancer, and severity of disease.
Regression Analysis of Self-efficacy by Age
Subsequent analysis indicated that age was the strongest predictor of type of Internet
use, with those over the age of 64.5 more likely to say they were not Internet users
compared with their younger counterparts. Because these groups were statistically
significantly different, separate logistic regression analyses were completed for subjects that were lessthan and over 64.5 years of age. In those over the age of 64.5,
overall regression analysis, which included all self-efficacy measures as well as significant subject characteristics, show that the same self-efficacy measures that are significant in the analysis of the entire sample are also significant in this subset of
subjects (actively participating in treatment decisions—p < :04, asking physicians
questions—p < :03, and sharing feelings of concern—p < :04; See Table 9). In addition, only income and educational status were significant predicting demographic
Table 9. Step down regression analysis of significant self-efficacy variables controlling
for demographic variables in subjects >age 64.5
Variable
Actively participating in treatment decisions
Asking physicians questions
Sharing feelings of concern
Odds ratio
95% CI
P-value
1.31
1.39
1.21
1.02–1.69
1.04–1.87
1.01–1.44
0.041
0.024
0.04
230
S. B. Bass et al.
Table 10. Step down regression analysis of significant self-efficacy variables controlling
for demographic variables in subjects <age 64.5
Variable
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Maintaining independence
Actively participating in treatment decisions
Asking physicians questions
Odds ratio
95% CI
P-value
1.17
1.23
1.36
0.98–1.40
1.01–1.50
1.10–1.69
0.09
0.035
0.007
characteristics, indicating that in an older population gender and age differences, as
well as severity of disease, are not strong predictors of level of Internet use.
In subjects under the age of 64.5, regression analyses also show the same selfefficacy measures to be statistically significant. In addition, in this younger subset,
the self-efficacy measure of confidence in maintaining independence was significant
and all subject characteristics except severity of disease were significant, indicating
that there still remains a difference in the degree to which subject characteristics predict type of Internet use. When controlling for subject characteristics, we found that
the self-efficacy variable of sharing feelings of concern is no longer significant, indicating that age is probably a confounding factor and not predictive of the true
relationship between self-efficacy and type of Internet use. The other significant
self-efficacy measures continue to be significant (maintaining independence—
p < :09, actively participating in treatment decisions—p < :04, and asking questions
of physicians—p < :007; see Table 10).
Discussion
The three most significant findings follow: (1) the change in type of Internet use over
the 8-week period from baseline to follow-up; (2) the significant relationship between
level of Internet use with patient behavior; and (3) the significant relationship
between level of Internet use and self-efficacy. Each of these findings is discussed
below.
Change in Type of Internet Use and Reasons for Internet Use
Qualitative responses of subjects to questions about why they changed their pattern
of use of Internet health information leads us to speculate that being diagnosed with
a serious and life-threatening disease such as cancer spurs people to actually go online and seek information, especially if they have easy access to a computer. The reasons for this were articulated by the subset of subjects (n ¼ 35) who responded to the
additional questions added to the survey. According to these subjects, the diagnosis
of cancer itself was the impetus to seek further information. Having friends and family encouraging newly diagnosed cancer patients to seek information was also an
important factor, whether those persons urged patients to get the information themselves (direct use) or offered to get the information for them (indirect use). This finding is important because it suggests that access to a computer, per se, does not
necessarily predict level of use of Internet health information. It may take an extreme
situation, like the diagnosis of cancer, as well as access and social networks that are
Use of Internet Information by Cancer Patients
231
oriented toward using Internet health information, to change previous Internet
health information usage patterns.
To better understand these findings, additional analyses investigated whether
there were differences between subjects who changed their type of Internet use
and those who did not. Interestingly, there were no statistically significant differences found between those who changed their Internet use level and the study variables, except age. Neither were there significant differences in type of Internet use by
patient task behavior or self-efficacy variables. Either there are no differences
between participants who changed Internet use groups and those who did not or differences are not captured by the study variables investigated.
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Patient Task Behavior
Results show that the type of use of Internet health information is associated with a
number of patient behaviors. Significantly more direct and indirect users said that
they prepared lists of questions for their doctors, compared with nonusers. Likewise,
direct users reported that they asked significantly more questions, with half of the
group responding that they asked six or more questions, the highest level of questioning on the survey instrument. These results suggest that use of Internet health
information is related to patient question asking behavior in a significant way. Having access to a large amount of health information could be viewed as an impetus to
ask questions about treatments and recommendations, not relying on the provider
only to supply medical information.
Direct and indirect users are also more likely than nonusers to say that they have
a ‘‘partnership’’ with their physician. This indicates that retrieving Internet health
information might influence patients’ feelings about their knowledge of disease,
making them perceive themselves as more able to enter into a partnership with their
physicians than those who are less informed. As patient advocacy groups have suggested, providing patients with access to medical information is empowering—if so,
it is reasonable to expect that using the Internet to find information will have some
type of impact on these patients’ relationships with their doctors.
Another interesting finding was that nonusers reported a higher rate of compliance with treatment recommendations than either indirect or direct users, an
inverse relationship with type of Internet use. While all three groups reported high
compliance rates, the differences may indicate that access to a wide range of medical
information, particularly information on cancer treatments that are often controversial, might reduce reported compliance. For instance, if a doctor recommends a
treatment and a patient has no other information on which to base a decision, he
or she might be more apt to comply with that recommendation. A patient who
can compare a doctor’s recommendation to legitimate alternative sources found
on the Internet, however, might less readily comply and be more comfortable questioning the recommendations of the physician. While this finding does not seem to
support the hypothesis that Internet use increased task behavior, one could interpret
the finding as a patient may be exhibiting more task behavior by questioning recommendations and not taking the advice of a doctor in blind faith. This finding has significant implications for doctor–patient relations and for medical education.
Empirical data based on Patient–Provider Communication theory has identified
several implications for the effect Internet health information use may have on patient
populations and that are supported by this study. The concept of task behavior is
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S. B. Bass et al.
very important for determining the ability of patients to develop ‘‘ownership’’ over
disease and feel they are involved in the process of deciding on treatments and
courses of action. Studies that have used interventions to try and influence patient
task behavior have been successful in increasing the number and quality of patient
questions, information recall, compliance with follow-up appointments, and satisfaction measures (see, for example, Brown, Bratton, Cabana, Kaciroti, & Clark, 2004;
Brown, Butow, Boyer, & Tattersall, 1999; Marcus et al., 1997). In this study, the
subject-initiated use of Internet health information clearly is related to increasing
the patient task behaviors of question asking, list making, and research gathering.
This suggests that the Internet has the potential to have a significant impact on public health education and the tailoring of health communication messages. The type of
Internet use is likely to have growing implications on doctor–patient relationships as
patients come to the patient–provider dyad armed with information and feeling more
in charge of their health. As theorized, changes in patient task behavior will necessitate real changes in the ways physicians communicate with their patients. The results
of this study, along with those conducted with similar patient populations, should
encourage researchers to study how interactive technologies might be used to benefit
patients with serious and life-threatening conditions and how their use might change
patient behavior.
Perceived Self-efficacy
The relationship between type of Internet use and self-efficacy also has implications
for cancer care. In this study, 3 of the 14 measures were statistically significantly
associated with type of Internet use (actively participating in treatment decisions,
asking physicians questions, and sharing feelings of concern), when controlling for
subject characteristics and in separate age groups. If the relationships found between
type of Internet use and self-efficacy are replicated in other studies, accessing and
using Internet health information may help patients understand their disease and
participate more fully in managing their treatment.
These results support self-efficacy theory and can be useful in explaining how
type of Internet health information use is related to patient behavior. Self-efficacy
theory posits that a person’s belief in his or her capabilities to accomplish something
will predict whether that person actually is able to change behavior. If self-efficacy
can be influenced, a patient can more effectively change behavior and continue to
gain confidence in being able to adhere to the change. Self-efficacy studies done with
cancer patients have shown that increasing feelings of self-efficacy have been positively related not only to behaviors such as screening and preventative behaviors
but also to survival, psychosocial adjustment, and general quality of life (see, for
example, Buller et al., 2000; De Nooijer, Lechner, Candel, & De Vries, 2004; de Vries &
Lechner, 2000; Eiser, Hill, & Blacklay, 2000; Jackson & Aiken, 2000; Kremers,
Mesters, Pladdet, van den Borne, & Stockbrugger, 2000; Martin, Froelicher, &
Miller, 2000; Myers et al., 2000; Reis, Trockel, King, & Remmert, 2004).
In this study, self-efficacy was related to Internet use in several ways, with both
direct and indirect users exhibiting higher self-efficacy on a number of measures,
compared with nonusers. Measures such as confidence in maintaining independence,
asking physicians questions, and actively participating in treatment decisions, show a
clear relationship to self-efficacy but also parallel key constructs in Patient–Provider
Communication theory. They also show a possible relationship with overall confidence
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Use of Internet Information by Cancer Patients
233
about feeling in control. While it is unknown whether patients who have these
social–psychological characteristics will have better health outcomes than those
who do not, these constructs suggest that it is important to understand how behavior, and perhaps disease trajectories, can be influenced by the use of Internet health
information.
What neither of these theories helps explain, however, is if there are inherent personality differences between people that make them more or less likely to be direct
users of Internet health information. For example, a perceived lower level of concern
among nonusers in this study, evidenced by their lower patient task behavior and
self-efficacy scores, might be explained by a number of personality or emotional
responses, including trust that the medical profession will appropriately care for
them, trust that a higher power is determining their fate, denial about the seriousness
of their situation, reliance on others to help them emotionally through their illness,
or that these personality characteristics lead them to be more satisfied with having
less information and to rely on emotional buffers to help them get through their illness. This is consistent with the theory of ‘‘monitoring vs. blunting’’ (Miller, 1995,
1996), which provides an explanation for why some patients seek out information
and others do not. This also is supported by the findings of a recent study with
another CIS population, that tailored health communication messages to women
who had high ‘‘need for cognition’’ scores, that is, they enjoy thinking deeply about
issues (Williams-Piehota, Schneider, Pizarro, Mowad, & Salovey, 2003). WilliamsPiehota and colleagues found that when messages were tailored for this population
of women, motivation for using mammography was increased. It stands to reason
that those without a high ‘‘need for cognition’’ would not respond to these tailored
messages in the same way.
Study Limitations and Strengths
This is a cross-sectional study and cannot infer causality; thus subsequent studies will
have to support these findings by using a different research design. Another major
limitation of the study is selection bias. While all subjects could be defined as information seekers due to the manner in which they were recruited, there are also biases
in the demographic characteristics of the sample. The sample is almost entirely composed of White, middle-class individuals. Such persons have been shown in other
studies to exhibit higher health self-efficacy and not to feel isolated from the health
care system and its providers.
It also could be argued, however, that the differences that have been found in
such a homogeneous population are socially and clinically significant. The relationships found between type of Internet use and the study variables may be important
for understanding the needs and behaviors of a significant subset of the population
of newly diagnosed cancer patients. For example, if most of the sample has high selfefficacy characteristics, or are health information seekers, and these characteristics
are seen across the Internet user groups, the differences observed could provide evidence of the Internet’s role in influencing how participants perceive their disease and
their role in managing it.
The major strength of this research is its unique contribution to the literature on
patient–provider relations and to the empirical research on patient task behavior and
self-efficacy. To date, no research has been published that addresses the relationship
between types of Internet use and patient behavior or self-efficacy. While others have
234
S. B. Bass et al.
attempted to understand the quantity of Internet use by a patient population or the
types of information sought by patient populations, this study provides empirical
data on newly diagnosed cancer patients’ actual use of the Internet, their perceptions
of why they used it, and what relationship it has with their physician relationships
and behavior.
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Conclusion
As one of the first empirical studies of how patients with a serious and life-threatening
condition use new information technology, this survey provides insight into how such
patient populations are likely to adopt information-seeking strategies in the future.
While much has been discussed in the literature about the quality of Internet health
information (Adams, 2003; Craigie, Loader, Burrows, & Muncer, 2002; Eysenbach &
Kohler, 2002; Eysenbach, Powell, Kuss, & Sa, 2002) and the lack of consistent
measures for Internet content (Gagliardi & Jadad, 2002; Risk & Dzenowagis, 2001;
Winker et al., 2000), there is little beyond speculation about the ways that information, good or bad, is being used by patients to manage their health. This study provides insight into how the use of Internet health information may be affecting a
specific patient population, which in turn may influence key features of doctor–
patient relationships. The implications of these findings are that health communication researchers need to study not only the technology itself, but also to understand
how technology affects both the delivery of the message and doctor–patient behaviors.
To date, almost all of the studies looking at the use of interactive media versus
more traditional avenues of message dissemination (brochure, fact sheet, video) have
shown the superiority of using interactive media (Bass, 2003). New technology gives
practitioners the ability to tailor information to the needs of the receiver almost
instantly and eliminates the time it takes for message development processes to meet
the needs of different audiences. What is lacking, however, is a solid empirical
research base from which to evaluate how these new strategies actually work, how
users understand the information they get, and how the technology contributes to
overall confidence for participating in health decision making, particularly where
patients face serious and life-threatening conditions. Further study to define and
prove these relationships will allow physicians, public health practitioners, and other
health information providers the ability to best equip their patients with the tools
they need to make appropriate decisions.
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