Internet Health Information Seeking Behavior and
Antiretroviral Adherence in Persons Living with HIV/AIDS
Lipika Samal, M.D., M.P.H.,1Somnath Saha, M.D., M.P.H.,2,3Geetanjali Chander, M.D., M.P.H.,4
P. Todd Korthuis, M.D., M.P.H.,5Rashmi K. Sharma, M.D., M.H.S.,6Victoria Sharp, M.D.,7
Jonathan Cohn, M.D., M.S.,8Richard D. Moore, M.D., M.H.S.,4and Mary Catherine Beach, M.D., M.P.H.4
While the Internet has the potential to educate persons living with HIV/AIDS (PLWHA), websites may contain
inaccurate information and increase the risk of nonadherence with antiretroviral therapy (ART). The objectives
of our study were to determine the extent to which PLWHA engage in Internet health information seeking
behavior (IHISB) and to determine whether IHISB is associated with ART adherence. We conducted a survey of
adult, English-speaking HIV-infected patients at four HIV outpatient clinic sites in the United States (Baltimore,
Maryland; Detroit, Michigan; New York, and Portland, Oregon) between December 2004 and January 2006. We
assessed IHISB by asking participants how much information they had received from the Internet since ac-
quiring HIV. The main outcome was patient-reported ART adherence over the past three days. Data were
available on IHISB for 433 patients, 334 of whom were on ART therapy. Patients had a mean age of 45 (standard
error [SE] 0.45) years and were mostly male (66%), African American (58%), and had attained a high school
degree (73%). Most (55%) reported no IHISB, 18% reported some, and 27% reported ‘‘a fair amount’’ or ‘‘a great
deal.’’ Patients who reported higher versus lower levels of IHISB were significantly younger, had achieved a
higher level of education, and had higher medication self-efficacy. In unadjusted analyses, higher IHISB was
associated with ART adherence (odds ratio [OR], 2.96, 95% confidence interval [CI] 1.27–6.94). This association
persisted after adjustment for age, gender, race, education, clinic site, and medication self-efficacy (adjusted odds
ratio [AOR] 2.76, 95% CI 1.11–6.87). Our findings indicate that IHISB is positively associated with ART ad-
herence even after controlling for potentially confounding variables. Future studies should investigate the ways
in which Internet health information may promote medication adherence among PLWHA.
cessful in conditions such as diabetes, heart failure, obesity,
and substance abuse, among others.1–7In addition to medical
applications delivered via the Internet, the Internet is be-
coming a major source of consumer health information. Sur-
veys indicate that 64% ofall Internet users in theUnited States
engage in Internet health information seeking behavior
he Internet promises to change HIV care. Internet-
based health interventions have been proven to be suc-
(IHISB) and that 4% of all Internet searches are health relat-
ed.8,9Studies have shown that persons living with HIV/AIDS
(PLWHA) and their caregivers use the Internet to seek infor-
mation about HIV.10,11Internet HIV support groups have
been shown to provide both emotional and informational
support for PLWHA.12However, there are also studies sug-
gesting Internet use may be associated with increased high-
risk sexual behavior.13,14
The vulnerability of some groups to inaccurate information
is a potential threat to optimal antiretroviral therapy (ART)
1Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts.
2Section of General Internal Medicine, Portland VA Medical Center, Portland, Oregon.
3Division of General Internal Medicine & Geriatrics,5Departments of Medicine and Public Health & Preventive Medicine, Oregon Health
Science University, Portland, Oregon.
4Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
6Division of Hospital Medicine, Northwestern University, Chicago Illinois.
7HIV Center for Comprehensive Care, Saint Lukes-Roosevelt Hospital, New York, New York.
8Division of Infectious Diseases, Wayne State University, Detroit, Michigan.
AIDS PATIENT CARE and STDs
Volume 25, Number 7, 2011
ª Mary Ann Liebert, Inc.
adherence. One study found that PLWHA—particularly
those with lower incomes, lower educational attainment,
poorer reading comprehension, and lower literacy levels—
assign credibility to websites that medical professionals do
not.15Younger age is associated with both lower IHISB and
lower adherence.16,17Another study showed that PLWHA
who use the Internet are more likely to believe that HIV
treatments ‘‘do more harm than good’’ and that HIV does not
cause AIDS.18Therefore, websites have the potential to edu-
cate patients, but they may also contain inaccurate informa-
tion and negatively impact adherence. Furthermore, if IHISB
is beneficial, there is concern that disparities in Internet use
could widen disparities in health outcomes, since low-income
PLWHA are less likely to use the Internet.19On the other
hand, one study found that training patients in a community
center improves IHISB both in frequency of use and in health
information evaluation skills.20Thus, if IHISB is proven to be
beneficial, it may be an important source of information and
support for PLWHA.
Several studies found mixed results with respect to the
association of IHISB with medication adherence. One study
found that persons who had used the Internet were more
likely to respond correctly to HIV knowledge questions, but
were not more likely to report ART adherence, while another
study found that Internet health information seekers reported
information to both positively and negatively affect PLWHA,
and the lack of empirical clarity about the influence of IHISB
on ART adherence, we sought to determine the association
between IHISB and ART adherence.
This was a cross-sectional study using baseline data from
the Enhancing Communication and HIV Outcomes (ECHO)
study, a multisite study examining potential sources of dis-
parities in HIV care, conducted between December 2004 and
January 2006. The design of the ECHO study has been pre-
viously published.23–26Briefly, study subjects were HIV pro-
viders and their patients at four HIV outpatient clinic sites in
the United States (Baltimore, Maryland; Detroit, Michigan;
New York, and Portland, Oregon). Patients were eligible for
inclusion if they were HIV-infected, 18 years or older, and
English-speaking. Researchassistants enrolled approximately
10 patients per participating provider from clinic waiting
rooms. Patients gave informed consent, and, following the
medical encounter, research assistants administered a 1-h
interview that assessed demographic, social, and behavioral
characteristics, including IHISB, ART adherence, health
beliefs, and medication self-efficacy. The study received
Institutional Review Board (IRB) approval from each of the
We measured IHISB, the independent variable in this
study, using the following question: ‘‘Now I’m going to read
you a list of sources that people sometimes use for informa-
tion about HIV/AIDS diagnosis and treatment. For each
source, please tell me how much information you have re-
ceived since you first tested HIV-positive: the Internet?’’
(Examples of other sources listed were main HIV doctor,
AIDS Service Organizations and personal acquaintances who
have HIV/AIDS.)27Response options were: ‘‘none,’’ ‘‘some,’’
‘‘fair amount,’’ and ‘‘great deal.’’ We examined the data dis-
tribution of frequency of IHISB. Based on a skewed distribu-
tion of the data, we combined the responses of ‘‘fair amount’’
and ‘‘great deal’’ into a single category.
Our outcome variable was ART adherence, restricted to
those patients on ART. Patients listed each ART medication
by name and regimen, and reported how many doses they
had missed in the past 3 days.28We created a dichotomous
reported missing any dose within the past 3 days.28
We collected information on age, gender, race/ethnicity,
and educational attainment (highest level attained from the
following choices: less than high school, high school degree,
To measure patient race/ethnicity, we asked patients to
Table 1. Study Sample
Internet health information seeking behavior
Patient characteristicsAll, n=433None, n=236Some, n=79Fair amount/great deal, n=118
Age (yrs)—mean (SE)
‡High school degree—n (%)
High medication self-efficacy—n (%)
Positive ART health beliefs—n (%)
On ART—n (%)
45 (0.45)47 (0.59)44 (1.2)44 (0.86)a
aWhen compared to ‘‘None’’ IHISB category, after controlling for site and accounting for clustering by provider, p<0.01.
bWhen compared to ‘‘None’’ IHISB category, after controlling for site and accounting for clustering by provider, p<0.001.
IHISB, health information seeking behavior; SE, standard error; ART, antiretroviral therapy.
446 SAMAL ET AL.
identify: (1) whether they were Hispanic/Latino, or not and
(2) to which racial group(s) they belonged. We then asked
them to identify—from a list of options including white/
American Indian/Alaska Native, Asian, Pacific Islander/
Native Hawaiian, or Other—a single, main racial/ethnic
group with which they identified themselves. Our variables
for patient race were derived from this question.
To evaluate the possibility that any observed association
between IHISB and ART adherence was explained by other
patient characteristics that might be associated with both
IHISB and adherence, we examined additional variables as
potential confounders: beliefs about ART and self-efficacy in
managing HIV medication regimens. We constructed a mea-
sure of ART-related health beliefs using five items addressing
the respondent’s beliefs about ART [(1) HIV drugs will im-
prove my health; (2) HIV drugs will help me have fewer
symptoms; (3) HIV drugs will keep me alive longer; (4) I will
get sick if I don’t take my HIV drugs; (5) It is important that
I take all doses of my HIV medicine]. Because the data dis-
tribution was heavily skewed toward positive responses we
dichotomized responses between those who responded
‘‘strongly agree’’ to all five statements and those who did not.
For medication self-efficacy, we used a validated 6-item self-
efficacy scale regarding management of HIV medications.29
Scores on the medication self-efficacy scale ranged from
1 to 10, with higher scores indicating greater medication self-
efficacy. Because the distribution was heavily skewed toward
the highest scores, we dichotomized responses between those
who scored 10 and those who did not.
We used bivariate multinomial logistic regression to test
the association of socio-demographic and behavioral charac-
teristics with IHISB. We then used logistic regression to test
the association of IHISB with ART adherence in both unad-
justed and adjusted analyses. We included potentially con-
founding sociodemographic and behavioral covariates in
multivariate models if the covariate was significant at a p
value of 0.10 in bivariate analysis or conceptually related to
either IHISB or ART adherence. All reported analyses con-
trolled for site as a fixed effect and accounted for clustering by
provider using generalized estimating equations to produce
standard error estimates.
Of 617 eligible patients, 435 (73%) agreed to participate and
completed all study procedures. The most common reasons
for patient refusal were not enough time to complete the in-
terview (n=106), not feeling well (n=22), and not being in-
terested (n=13). Data on IHISB were available for 433
Study sample characteristics and frequency of IHISB are
shown in Table 1.
Respondents had a mean age of 45 years. Most of the re-
spondents were male (66%) and had a high school degree
(73%) Fifty-eight percent were African American and 24%
were non-Hispanic white. More than half of respondents
(55%) reported no IHISB, while 18% reported some IHISB
and 27% reported ‘‘a fair amount’’ or ‘‘a great deal’’ of IHISB.
Compared to those with no IHISB, those reporting a fair
amount/great deal of IHISB were more likely to have a
high school education (p=0.001). Younger patient age
was also associated with IHISB; on average, those reporting
a fair amount/great deal IHISB were 3 years younger
Higher medication self-efficacy was associated with a
higher likelihood of IHISB (p=0.000) in bivariate analyses.
There wasno significant association between IHISB and ART-
related health beliefs.
in Table 2. Those who reported a fair amount/great deal of
IHISB had a nearly threefold greater odds of adherence than
those who reported none (OR 2.91, 95% CI 1.22–6.95). This
association persisted after adjustment for age, gender, race,
and education (AOR 3.13, 95% CI 1.31–7.45). After additional
adjustment for medication self-efficacy, IHISB remained sig-
nificantly associated with ART adherence (AOR 2.76, 95% CI
In this sample of patients engaged in HIV care, nearly half
reported IHISB. Those who accessed the Internet for health
information a ‘‘fair amount’’ or a ‘‘great deal’’ were more
Table 2. Association of Internet Health Information Seeking Behavior with ART Adherence
(95% CI) n=334
(95% CI) n=333
(95% CI) n=333
Fair amount/great deal
aAmong patients on ART.
bAdjusted for clinic site and accounting for clustering by provider.
cAdjusted for age, gender, race, education, clinic site, and accounting for clustering by provider.
dAdjusted for medication self-efficacy, age, gender, race, education, clinic site, and accounting for clustering by provider.
OR, odds ratio; CI, confidence interval; ART, antiretroviral therapy; IHISB, Internet health information seeking behavior.
INTERNET AND ADHERENCE TO ANTIRETROVIRALS 447
likely than nonusers to report that they had not missed any
ART doses in the 3 days preceding their routine appointment.
IHISB was associated with ART adherence independent of
other factors, such as educational attainment, age, and race.
adherence but rather served as a marker of greater patient
activation, or greater interest in and enthusiasm for ART, we
evaluated measures of self-efficacy and ART-related beliefs as
potential confounders. We found that ART-related beliefs
were not associated with IHISB, and that medication self-
efficacy, although associated with IHISB, did not explain the
relationship between IHISB and adherence. These findings
suggest that IHISB is not merely serving as a proxy for these
There are several possible explanations for an association
between IHISB and adherence. It may be causal or due to un-
measured confounding factors. Both possibilities are worth
consideration. The concept that IHISB could increase patient
adherence issupportedbyqualitativestudies that explore how
inform treatment decisions.10,30,31The Internet may provide
tools and methods to help with complicated medication regi-
mens. For example, the online social networking site Patient-
sLikeMe goes well beyond a message board for sharing
experiences; it includes a drug database to help patients accu-
rately record the treatments they are taking and a visual dis-
play of their treatment history.32PLWHA who use the site
report an impact on the decision to begin ART and better un-
derstanding of the consequences of taking a ‘‘drug holiday.’’33
On the other hand, ours was a cross-sectional study, so
there may have been unmeasured or residual confounding of
the relationship between IHISB and adherence. The slight
reduction in the IHISB-adherence association after adjusting
for self-efficacy suggests that this finding may be partly ex-
plained by greater self-efficacy among patients reporting
IHISB. General motivation and self-efficacy may make people
more likely to seek information, from the Internet or other-
wise, and also to be adherent to treatment regimens. Alter-
nately, the reduction in the point estimate could reflect
mediation of the IHISB-adherence association by self-efficacy.
in turn, to greater adherence. Whether the association is
causal or not, IHISB appears to be an important marker for
greater adherence among PLWHA, even after adjusting for
factors like education, clinic site, and medication self-efficacy.
Differences between our findings and those of prior studies
that explored similar questions merit further discussion. In
one study, Kalichman et al.22found an association between
IHISB and health beliefs, but not between IHISB and adher-
ence. There are a number of possible reasons that our results
were different. First, the instrument we used to measure ART-
related health beliefs consisted of self-reflective and general
questions (e.g., ‘‘It is important that I take all doses of my HIV
medicine.’’), while Kalichman and colleages used factual and
specific questions (e.g., ‘‘Is AZT a protease inhibitor?’’). Sec-
ond, the patients in our study were slightly older and more
racially diverse. Third, participants in the previous study
were recruited from a variety of settings including social
service agencies, AIDS service organizations, community
residences for PLWHA, health care providers, and infectious
disease clinics, whereas we recruited only from HIV clinics.
Further research clarifying whether differing associations of
IHISB with adherence across studies relates to different pa-
tient populations, settings, time frames, or other contextual
factors may informhow the Internet mightbe most effectively
used to promote adherence and improve outcomes among
There are several potential limitations of this study. We
used a single, self-reported measure to assess ART adherence.
Although this measure has been previously validated, addi-
tional studies should consider evaluating other measures of
adherence, such as pharmacy records, Medication Event
Monitoring System caps, and virologic outcomes.28The cross-
sectional design did not allow us to assess how IHISB and
ART adherence may change over time. Although our study
geographic regions within the United States, it may not be
generalizable to PLWHA in rural areas of the United States,
those using non-English websites around the world, and
those not engaged in medical care. Finally, the question used
to measure IHISB may have been interpreted as a question
about general Internet use rather than health information
seeking behavior. However, the context of the question (ask-
ing how much information about HIV diagnosis and treat-
ment was obtained from the respondent’s main HIV doctor,
AIDS service organizations, and other PLWHA) makes this
Our findings indicate that IHISB is associated with self-
reported ART adherence. This association may reflect an
actual benefit of Internet health information, or that IHISB
reflects greater personal engagement in managing one’s
illness, which in turn predicts greater ART adherence. Given
the prevalence of IHISB among PLWHA, development of
accurate, patient-centered HIV health education websites
may prove to be an important tool for improving health
outcomes among PLWHA. Future research should aim to
empower PLWHA to find relevant and trustworthy infor-
mation on the Internet and determine whether these efforts
can improve ART adherence and other clinical outcomes.
This research was supported by a contract from the Health
Resources and Service Administration and the Agency
for Healthcare Research and Quality (AHRQ 290-01-0012).
Dr. Korthuis was supported by the National Institute of Drug
Abuse (K23 DA019809), Dr. Chander was supported by the
National Institute on Alcoholism and Alcohol Abuse (K23
AA015313), Dr. Saha was supported by the Department of
Veterans Affairs, Dr. Beach was supported by the Agency for
Drs. Beach and Saha were supported by Robert Wood John-
son Generalist Physician Faculty Scholars Awards.
Author Disclosure Statement
No competing financial interests exist.
1. Benhamou PY, Melki V, Boizel R, et al. One-year efficacy
and safety of web-based follow-up using cellular phone in
type 1 diabetic patients under insulin pump therapy: The
PumpNet study. Diabetes Metab 2007;33:220–226.
448SAMAL ET AL.
2. Jackson CL, Bolen S, Brancati FL, Batts-Turner ML, Gary TL.
A systematic review of interactive computer-assisted tech-
nology in diabetes care. Interactive information technology
in diabetes care. J Gen Intern Med 2006;21:105–110.
3. Linke S, Murray E, Butler C, Wallace P. Internet-based in-
teractive health intervention for the promotion of sensible
drinking: Patterns of use and potential impact on members
of the general public. J Med Internet Res 2007;9:e10.
4. Riper H, Kramer J, Smit F, Conijn B, Schippers G, Cuijpers P.
Web-based self-help for problem drinkers: A pragmatic
randomized trial. Addiction 2008;103:218–227.
5. Kypri K, Langley JD, Saunders JB, Cashell-Smith ML, Her-
bison P. Randomized controlled trial of web-based alcohol
screening and brief intervention in primary care. Arch Intern
6. Kashem A, Droogan MT, Santamore WP, Wald JW, Bove
AA. Managing heart failure care using an Internet-based
telemedicine system. J Card Fail 2008;14:121–126.
7. Neve M, Morgan PJ, Jones PR, Collins CE. Effectiveness of
web-based interventions in achieving weight loss and weight
loss maintenance in overweight and obese adults: A system-
atic review with meta-analysis. Obes Rev 2010;11:306–321.
8. Hesse BW, Nelson DE, Kreps GL, et al. Trust and sources of
health information: The impact of the Internet and its im-
plications for health care providers: Findings from the first
health information national trends survey. Arch Intern Med
9. Eysenbach G, Kohler C. What is the prevalence of health-
related searches on the world wide web? Qualitative and
quantitative analysis of search engine queries on the Inter-
net. AMIA Annu Symp Proc 2003:225–229.
10. Courtenay-Quirk C, Horvath KJ, Ding H, et al. Perceptions
of HIV-related websites among persons recently diagnosed
with HIV. AIDS Patient Care STDs. 2010;24:105–115.
11. Horvath KJ, Courtenay-Quirk C, Harwood E, et al. Using
the Internet to provide care for persons living with HIV.
AIDS Patient Care STDs 2009;23:1033-1041.
12. Mo PK, Coulson NS. Exploring the communication of social
support within virtual communities: A content analysis of
messages posted to an online HIV/AIDS support group.
Cyberpsychol Behav 2008;11:371–374.
13. Stupiansky NW, Rosenberger JG, Schick V, Herbenick D,
Novak DS, Reece M. Factors associated with sexually
transmitted infection testing among men who utilize an In-
ternet-based men who have sex with men community. AIDS
Patient Care STDs 2010;24:713–717.
14. Schnarrs PW, Rosenberger JG, Satinsky S, et al. Sexual
compulsivity, the Internet, and sexual behaviors among men
in a rural area of the united states. AIDS Patient Care STDs
15. Benotsch EG, Kalichman S, Weinhardt LS. HIV-AIDS pa-
tients’ evaluation of health information on the Internet: The
digital divide and vulnerability to fraudulent claims. J
Consult Clin Psychol 2004;72:1004–1011.
16. Fogarty L, Roter D, Larson S, Burke J, Gillespie J, Levy R.
Patient adherence to HIV medication regimens: A review of
published and abstract reports. Patient Educ Couns 2002;
17. Flicker S, Goldberg E, Read S, et al. HIV-positive youth’s
perspectives on the Internet and e-health. J Med Internet Res
18. Kalichman SC, Eaton L, Cherry C. ’There is no proof that
HIV causes AIDS": AIDS denialism beliefs among people
living with HIV/AIDS. J Behav Med 2010;33:432–440.
19. Mayben JK, Giordano TP. Internet use among low-income
persons recently diagnosed with HIV infection. AIDS Care
20. Kalichman SC, Cherry C, Cain D, et al. Internet-based health
information consumer skills intervention for people living
with HIV/AIDS. J Consult Clin Psychol 2006;74:545–554.
21. Kalichman SC, Cain D, Cherry C, Pope H, Eaton L, Kalich-
man MO. Internet use among people living with HIV/AIDS:
Coping and health-related correlates. AIDS Patient Care
22. Kalichman SC, Benotsch EG, Weinhardt LS, Austin J, Luke
W. Internet use among people living with HIV/AIDS: As-
sociation of health information, health behaviors, and health
status. AIDS Educ Prev 2002;14:51–61.
23. Kumar R, Korthuis PT, Saha S, et al. Decision-making role
preferences among patients with HIV: Associations with
patient and provider characteristics and communication
behaviors. J Gen Intern Med 2010;25:517–523.
24. Beach MC, Saha S, Korthuis PT, et al. Differences in patient-
provider communication for hispanic compared to non-
Hispanic white patients in HIV care. J Gen Intern Med
25. Beach MC, Saha S, Korthuis PT, et al. Patient-provider
communication differs for black compared to white HIV-
infected patients. AIDS Behav (in press).
26. Korthuis PT, Saha S, Chander G, et al. Substance use and the
quality of patient-provider communication in HIV clinics.
AIDS Behav (in press).
27. Bozzette SA, Berry SH, Duan N, et al. The care of HIV-
infected adults in the united states. HIV cost and services
utilization study consortium. N Engl J Med 1998;339:1897–
28. Chesney MA, Ickovics JR, Chambers DB, et al. Self-reported
adherence to antiretroviral medications among participants
in HIV clinical trials: The AACTG adherence instruments.
patient care committee & adherence working group of the
outcomes committee of the adult AIDS clinical trials group
(AACTG). AIDS Care 2000;12:255-266.
29. Shively M, Smith TL, Bormann J, Gifford AL. Evaluating
self-efficacy for HIV disease management skills. AIDS Behav
30. Reeves PM. How individuals coping with HIV/AIDS use
the Internet. Health Educ Res 2001;16:709–719.
31. Kremer H, Ironson G. People living with HIV: Sources of
information on antiretroviral treatment and preferences for
involvement in treatment decision-making. Eur J Med Res
32. Frost JH, Massagli MP. Social uses of personal health in-
formation within PatientsLikeMe, an online patient com-
munity: What can happen when patients have access to one
another’s data. J Med Internet Res 2008;10:e15.
33. Frost J, Okun S, Vaughan T, Heywood J, Wicks P. Patient-
reported outcomes as a source of evidence in off-label
prescribing: Analysis of data from PatientsLikeMe. J Med
Internet Res 2011;13:e6.
Address correspondence to:
Lipika Samal, M.D., M.P.H.
1620 Tremont Street
Boston, MA 02120-1613
INTERNET AND ADHERENCE TO ANTIRETROVIRALS 449
Page 6 Download full-text