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In the era of eHealth, eHealth literacy is emerging as a key concept to promote self-management of chronic conditions such as HIV. However, there is a paucity of research focused on eHealth literacy for people living with HIV (PLWH) as a means of improving their adherence to HIV care and health outcome.
The objective of this study was to critically appraise the types, scope, and nature of studies addressing eHealth literacy as a study variable in PLWH.
This systematic review used comprehensive database searches, such as PubMed, EMBASE, CINAHL, Web of Science, and Cochrane, to identify quantitative studies targeting PLWH published in English before May 2017 with eHealth literacy as a study variable.
We identified 56 unique records, and 7 papers met the eligibility criteria. The types of study designs varied (descriptive, n=3; quasi-experimental, n=3; and experimental, n=1) and often involved community-based settings (n=5), with sample sizes ranging from 18 to 895. In regards to instruments used, 3 studies measured eHealth literacy with validated instruments such as the eHealth Literacy Scale (eHEALS); 2 studies used full or short versions of Test of Functional Health Literacy in Adults, whereas the remaining 2 studies used study-developed questions. The majority of studies included in the review reported high eHealth literacy among the samples. The associations between eHealth literacy and health outcomes in PLWH were not consistent. In the areas of HIV transmission risk, retention in care, treatment adherence, and virological suppression, the role of eHealth literacy is still not fully understood. Furthermore, the implications for future research are discussed.
Understanding the role of eHealth literacy is an essential step to encourage PLWH to be actively engaged in their health care. Avenues to pursue in the role of eHealth literacy and PLWH should consider the development and use of standardized eHealth literacy definitions and measures.
HIV is a major global health issue with an estimated 36.7 million people living with HIV (PLWH) worldwide [
eHealth, “a medical and public health practice supported by a Web-based platform,” is a popular innovation in self-management of chronic conditions and includes mobile phones, tablet computers, and personal computers [
eHealth literacy refers to one’s ability “to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to address or solve a health problem” [
To the best of our knowledge, this is the first systematic review to address eHealth literacy in PLWH. Although previous systematic reviews have addressed eHealth literacy in college students [
We conducted a systematic review of quantitative evidence designed to assess eHealth literacy as a study variable in PLWH. Owing to the heterogeneity relative to study designs and statistical analysis approaches among the included studies, we synthesized the study findings rather than conducting a meta-analysis.
Studies were screened to assess their relevance for our review. Specifically, the following inclusion criteria were used papers that used a quantitative study design (including descriptive, correlational, quasi-experimental, or experimental); papers including eHealth literacy as a study variable; and papers including participants with HIV or AIDS. Our initial search was not limited by the age of study participants or sex to maximize the breadth of the study findings. In addition, we included any study that reported quantitative findings relevant to the review question. Studies from around the globe were included, as were studies conducted in various settings, including community or health system settings.
Notably, only studies written in English were included. Studies were excluded if full-texts were unavailable (ie, conference abstracts), they were not quantitative designs, or they reported protocol only with no measured outcomes.
In consultation with a medical librarian, peer-reviewed journal papers were searched systematically in PubMed, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane databases using variations of MeSH terms—methodological interest (ie, measurement of eHealth literacy as a study variable), population of interest (ie, PLWH), and study design of interest (ie, quantitative) to identify relevant papers published in English before April 27, 2017. In addition, a manual search of reference lists in selected papers was completed.
At the conclusion of the study selection process, 1 reviewing author extracted data from the studies using a standard template. The initial data extraction captured both the study characteristics (eg, setting, participants, type of study design, and eHealth literacy measure) and key findings from each study. In addition, other team members reviewed the studies and extracted data relating to key findings. Extracted findings were compared and discussed until all discrepancies were resolved.
We assessed the rigor of the underlying evidence base for the review by developing an overview of key methodological characteristics, including the study design, sample size and strategy, study setting, and year of publication. No studies were excluded on the basis of the quality assessment. Rather, the quality assessment process was conducted independently by 2 raters using the Joanna Briggs Institute quality appraisal tools based specifically on study designs, randomized controlled trials (RCTs) [
Overall, the studies appraised in this review achieved, at least, the assessment criteria, but the quality varied across individual studies. Although 1 RCT scored 8 of 13 [
Furthermore, an interrater agreement rate was calculated [
Literature review flowchart.
Quality assessment.
Study items | Siedner et al [ |
Ownby et al [ |
Robinson et al [ |
Woods et al [ |
Blackstock et al [ |
Kim et al [ |
Krishnan et al [ |
|
1. Was true randomization used for assignment of participants to treatment groups? | ✔ | |||||||
2. Was allocation to treatment groups concealed? | ||||||||
3. Were treatment groups similar at the baseline? | ||||||||
4. Were participants blind to treatment assignment? | ||||||||
5. Were those delivering treatment blind to treatment assignment? | ||||||||
6. Were outcomes assessors blind to treatment assignment? | ||||||||
7. Were treatment groups treated identically other than the intervention of interest? | ✔ | |||||||
8. Was follow-up complete and, if not, were differences between groups in terms of their follow-up adequately described and analyzed? | ✔ | |||||||
9. Were participants analyzed in the groups to which they were randomized? | ✔ | |||||||
10. Were outcomes measured in the same way for treatment groups? | ✔ | |||||||
11. Were outcomes measured in a reliable way? | ✔ | |||||||
12. Was appropriate statistical analysis used? | ✔ | |||||||
13. Was the trial design appropriate, and any deviations from the standard randomized controlled trial design (individual randomization, parallel groups) accounted for in the conduct and analysis of the trial? | ✔ | |||||||
1. Is it clear in the study what is the “cause” and what is the “effect” (ie, there is no confusion about which variable comes first)? | ✔ | ✔ | ✔ | |||||
2. Were the participants included in any comparisons similar? | ✔ | |||||||
3. Were the participants included in any comparisons receiving similar treatment or care, other than the exposure or intervention of interest? | ||||||||
4. Was there a control group? | ✔ | |||||||
5. Were there multiple measurements of the outcome both pre and post the intervention or exposure? | ✔ | ✔ | ||||||
6. Was follow-up complete and, if not, were differences between groups in terms of their follow-up adequately described and analyzed? | ✔ | ✔ | ||||||
7. Were the outcomes of participants included in any comparisons measured in the same way? | ✔ | ✔ | ✔ | |||||
8. Were outcomes measured in a reliable way? | ✔ | ✔ | ✔ | |||||
9. Was appropriate statistical analysis used? | ✔ | ✔ | ✔ | |||||
1. Were the criteria for inclusion in the sample clearly defined? | ✔ | ✔ | ✔ | |||||
2. Were the study subjects and the setting described in detail? | ✔ | ✔ | ✔ | |||||
3. Was the exposure measured in a valid and reliable way? | ✔ | ✔ | ||||||
4. Were objective, standard criteria used for measurement of the condition? | ✔ | ✔ | ||||||
5. Were confounding factors identified? | ✔ | ✔ | ||||||
6. Were strategies to deal with confounding factors stated? | ✔ | |||||||
7. Were the outcomes measured in a valid and reliable way? | ✔ | ✔ | ||||||
8. Was appropriate statistical analysis used? | ✔ | ✔ | ✔ |
Study participants were recruited from a variety of settings as follows: community-based HIV/AIDS organizations [
Among studies that included women, most had a majority of female participants (56%-100%) but 2 [
In this review, 5 of 7 studies defined eHealth literacy. Most studies [
In addition, 3 studies conducted in the United States [
Moreover, Ownby et al [
Overview of included studies.
Study | Study design, sample size, and setting | Study purpose | Study framework | Sample characteristics | Definition of eHealth literacy |
Blackstock et al, 2016 [ |
Cross-sectional, N=63, February-April, 2014; 6 community-based organizations providing social and clinical services to people living with HIV | To examine the relationship between eHealth literacy and HIV transmission risk behaviors in internet-using women with HIV | No study framework reported | 100% female; median age, 49 (IQRa 44-54) years; 54.0% (34/63) non-Hispanic black; 36.5% (23/63) Hispanic; 38.1% (24/63) <high school education; 85.7% (54/63) prescribed ARTb; 87.3% (55/63) owned a cell phone; 58.7% (37/63) had a computer or tablet | “The ability to find, under-stand, & evaluate health information from electronic sources and apply this information to a specific health problem” (Norman and Skinner, 2006 [ |
Kim et al, 2015 [ |
Cross-sectional, June 2012-August 2013, N=895, AIDS Support Organization | To determine the proportion of people living with HIV who are literate and also use mobile phones in rural Uganda | No study framework reported | 76.4% (684/895) female; median age, 44 (IQR 44-50) years; 65% (581/895) <high school education; median time on HIV medications, 6.8 (IQR 5.8-7.7) years; 82.8% (741/895) owned a mobile phone; 73.0% (653/895) can read and write | Ability to read and write |
Krishnan et al, 2015 [ |
Cross-sectional, N=359, no specified date, 3 sites at 2 nongovernmental organizations providing health care | To examine the use of communication technology and acceptance of mHealth among HIV-infected Peruvian men who have sex with men and TGWc to gauge the feasibility of an mHealth-enabled HIV-risk reduction program | No study framework reported | 77.7% (279/359) male; 13.3% (48/359) TGW; mean age, 34 (SD 8.11) years; 2.2% (8/359) <high school education; 53.3% (131/246) completed college; 87.2% (313/359) currently on ART; 59.6% (214/359) had access to a standard cell phone; 30.1% (108/359) had access to a smartphone; 37.3% (134/359) used landlines; 35.4% (127/359) accessed a laptop or computer | Definition of eHealth literacy not reported |
Ownby et al, 2012 [ |
Quasi-experimental, N=124, May 2010-December 2011, Urban and suburban HIV clinics | To evaluate whether an Information-Motivation-Behavioral Skills Model–based electronic intervention can improve health literacy and medication adherence | Information-Motivation-Behavioral Skills model | 29% female (36/124); mean age, 47.1 (SD 8.69) years; 63% (78/124) black; 37% (46/124) <high school education; mean, 11.6 (SD 7.18) years on ART; mean Test of Functional Health Literacy in Adults score, 88.48 (SD 14.16) | “The degree to which individuals have the capacity to obtain, process, & understand basic health information & services needed to make appropriate health decisions” (Nielsen-Bohlman et al, 2004 [ |
Robinson et al, 2010 [ |
Quasi-experimental, N=18, July, 2008, HIV-positive care center in a hospital setting | To determine if computer skills and internet health educational intervention will improve the perceived knowledge of internet health resources and confidence using the internet for health questions | No study framework reported | 55.6% (10/18) female; mean age, 46 (range 34-69) years; 61.1% (11/18) African American; 27.8% (5/18) Caucasian; 44.4% (8/18) high school education or less; 72% (13/18) have regular internet access; 23% (3/13) sought health information in the internet in the past 3 months | The “capacity to acquire, understand & use information in ways which promote & maintain good health” |
Siedner et al, 2015 [ |
Experimental, N=385, HIV clinic of the Mbarara Regional Referral Hospital | To identify predictors of uptake of a mHealth app and evaluate the efficacy of various short message service text message formats to optimize the confidentiality and accessibility | Concepts derived from the Technology Acceptance Model and the Unified Theory of Technology Acceptance and Use of Technology | 65.2% (251/385) female; median age 32 (IQR 26-39) years; 62.4% (240/385) primary education or less; 67.5% (260/385) could read a complete sentence; 81.8% (315/385) had a mobile phone | Definition of eHealth literacy not reported |
Woods et al, 2016 [ |
Cross-sectional, N=67, neuroAIDS research center, which recruits from local HIV clinics and community-based organizations | To evaluate the effects of HIV-associated neurocognitive disorders on 2 internet-based tests of health care management | No study framework reported | 9.0% (6/67) female; 68.7% (46/67) HIV+ and 31.3% (21/67) HIV- mean age 45.5 (SD 9.2) years; 53.7% (36/67) Caucasian; 19.4% (13/67) Hispanic; mean education level 13.2 (SD 2.5) years; 95.7% (44/46) prescribed ART; 86.6% (58/67) use a home computer; 76.1% (51/67) own a smartphone; 67.2% (45/67) use the internet daily | “The capacity to obtain, communicate, process, & understand basic health information & services to make appropriate health decisions” (Patient Protection & Affordable Care Act, 2010 [ |
aIQR: interquartile range.
bART: antiretroviral therapy.
cTGW: transgender women.
Overall, varying scales with differing scoring systems were used to determine the level of eHealth literacy. High eHealth literacy scores among PLWH ranged from 52.4% to 87% in study samples with the majority of studies finding high eHealth literacy among 65%-80% of participants. Such a wide variance arose because high literacy was defined differently in each study, ranging from the ability to read a complete sentence [
The acceptability of eHealth interventions was measured in 3 studies [
In this review, 6 of 7 studies examined the associations between eHealth literacy and a variety of health outcomes in PLWH. The 2 studies that measured the relationship between eHealth literacy and HIV-related behavior reported conflicting results. In Blackstock et al [
In contrast, following an electronically delivered health literacy intervention targeting HIV-related health literacy on medication adherence, participants in Ownby et al [
Siedner et al [
Overview of the included studies.
Study | Measurement of eHealth Literacy (Validity or Reliability) | HIV-Related Health Outcome | Main Findings |
Blackstock et al, 2016 [ |
eHEALSa Dichotomized at the median (high vs low health literacy; alpha=.88) | HIV transmission risk behaviors, including condomless vaginal or anal intercourse, and any illicit drug use in the previous 30 days | Higher eHealth literacy, AORb 3.90 (95% CI 1.05-14.56), significantly associated with HIV transmission risk behaviors, adjusted for income and self-perceived health status. |
Kim et al, 2015 [ |
Study questions: “Are you able to read?” and “Are you able to write?” (validity or reliability not reported) | Viral suppression (CD4c count), adherence to ARTd | Literate mobile phone users had lower adherence to ART (84.2% vs 90.6%; |
Krishnan et al, 2015 [ |
Short Test of Functional Health Literacy in Adults (validity or reliability not reported) | ART adherence | No significant differences were found in communication technology use and mHealth acceptance among participants with alcohol use disorders, depression, and suboptimal ART adherence. |
Ownby et al, 2012 [ |
TOFHLAe<59, inadequate; 60-74, marginal; >75, adequate (validity or reliability not reported) | Medication adherence | Changes in the adherence only approached the statistical significance. Knowledge and behavioral skills increased over the course of the study. |
Robinson et al, 2010 [ |
eHEALS (validity or reliability not reported) | HIV-related health outcome not measured | A significant improvement from the baseline to immediately following the intervention was observed in perceived eHealth literacy levels (mean summary score 19 vs 32, |
Siedner et al, 2015 [ |
Participants were asked to read a complete sentence in the local language (validity or reliability not reported) | Retention in care defined as a return to the clinic within 7 days of the first SMSf text message for those with abnormal results or on the date of the scheduled appointment for those with normal results | The ability to read a complete sentence on enrollment was independently associated with accurate identification of the message sent, AOR 4.54 (95% CI 1.42-14.47), and return to the clinic within 7 d of the first transmitted SMS text message, AOR 3.81 (95% CI 1.61-9.03). An ability to access an SMS text message on enrollment was independently associated with returning to the clinic within 7 days of an abnormal SMS text notification, AOR 4.90 (95% CI 1.06-22.61). |
Woods et al, 2016 [ |
TOPSg; TOHRNh; eHEALS; Rapid Estimate of Adult Literacy in Medicine; HIV Knowledge 18; Expanded Numeracy Scale; TOFHLA; Short Assessment of Health Literacy; Newest Vital Sign (validity or reliability not reported) | CD4 count and HIV plasma viral load | Lower TOPS scores were associated with fewer years of education (ρ=.49, |
aeHEALS: eHealth Literacy Scale.
bAOR: adjusted odds ratio.
cCD4: cluster of differentiation 4.
dART: antiretroviral therapy.
eTOFHLA: Test of Functional Health Literacy in Adults.
fSMS: short message service.
gTOPS: Test of Online Pharmacy Skills.
hTOHRN: Test of Online Health Records Navigation.
The relationship between eHealth literacy and HIV treatment adherence was mixed. Literacy was inversely associated with ART adherence, which was measured by Kim et al [
Because only 2 studies assessed participants’ HIV viral load under dissimilar study settings, we were unable to determine the association between eHealth literacy and HIV viral suppression [
Although there has been limited reporting on eHealth literacy targeting PLWH, available studies addressing eHealth literacy in PLWH varied in their scope, methodology, and outcomes. The studies included in the systematic review provide some evidence for the role of eHealth literacy in relation to diverse HIV-related health outcomes, including HIV transmission risk, retention in care, treatment adherence, and virological suppression. Even though eHealth literacy was generally high and majority of those individuals included in the samples were receptive to the use of SMS text messaging communication [
In descriptive studies, eHealth literacy was either inversely associated with HIV transmission prevention behaviors, ART adherence, or viral load [
Negative outcomes in retention in care and treatment adherence may be attributed to general literacy challenges and access to phones, laptops, and desktop devices [
This review has revealed several gaps in the existing evidence base; gaps that collectively point to what we argue should be key parts of the eHealth literacy research agenda going forward. The most important gap and a critical focus of future research is the use of validated instruments to measure eHealth literacy, which do not appear in these studies. Much of the research we reviewed used some form of eHealth literacy assessment but with no evidence of validity and reliability or proxy measures for eHealth literacy. Future eHealth literacy research should adopt more rigorous instrumental approaches to addressing eHealth literacy as a new way of promoting and facilitating self-management in PLWH. In addition, there exists a limited explanation of definitions of eHealth literacy used in the literature. Hence, the selection of study instruments was minimally justified within the reviewed studies, highlighting the need for adopting a validated eHealth literacy framework to better understand and promote healthy behaviors and outcomes of PLWH. Finally, this review highlighted a critical methodological gap and area for future improvement—the need for ensuring a rigorous study design with adequate sample size, use of validated eHealth literacy measures and theoretical framework, and the use of diverse study samples of PLWH; for example, because >90% of adolescents and young adults use the internet daily [
Although the strengths of this review’s design included its inclusive search strategy that ensured extensive coverage, standardized data extraction, and iterative analysis, there are several limitations. First, despite our expanded search criteria, only a small number of studies met the inclusion criteria because of a lack of published studies. Second, the heterogeneity in the quality and quantity of data reported in the studies included in the review. Finally, we were unable to include studies in languages other than English, thereby limiting the generalizability of our findings.
In conclusion, the importance of eHealth literacy among PLWH has only recently begun to be addressed. In the areas of HIV transmission risk, retention in care, treatment adherence, and virological suppression, the role of eHealth literacy remains partially understood. Understanding the role of eHealth literacy among PLWH is an essential next step in self-management of HIV and AIDS. Avenues to pursue in the role of eHealth literacy and PLWH should include the development and use of standardized eHealth literacy measures. Additionally, examining the role of eHealth literacy longitudinally from prevention to viral suppression could yield knowledge regarding at what point, from diagnosis through management, are the best points to intervene with eHealth literacy strategies. Finally, elucidating the other factors that potentially contribute to eHealth literacy, such as access and general literacy, could yield valuable findings going forward.
Search strategy.
adjusted odds ratio
antiretroviral therapy
cluster of differentiation 4
eHealth Literacy Scale
interquartile range
people living with HIV
randomized controlled trials
short message service
Test of Functional Health Literacy in Adults
Test of Online Health Records Navigation
Test of Online Pharmacy Skills
The authors wish to thank Stella Seal for her assistance with paper search. The study was supported by a grant from the Hopkins Center for AIDS Research (P30 AI094189) and, in part, by a grant from the Dorothy Evans Lyne Fund. Additional resources were provided by the Center for Cardiovascular and Chronic Care at the Johns Hopkins University School of Nursing. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
None declared.