HLRP 0220 Cudjoe

Systematic Review
Empirically Tested Health Literacy Frameworks

Joycelyn Cudjoe, PhD, RN; Sabianca Delva, BSN, RN; Mia Cajita, PhD, RN-BC; and Hae-Ra Han, PhD, RN, FAAN
ABSTRACT
Background: Health literacy is a significant determinant of health behaviors, but the pathways through
which health literacy influences health behaviors are not completely clear nor consistent. The purpose of this
systematic review is to critically appraise studies that have empirically tested the potential pathways linking
health literacy to health behavior. Methods: We performed searches of the electronic databases PubMed, Em-
base, and CINAHL to identify studies that proposed a conceptual framework and empirically tested the pro-
posed mechanism through which health literacy influences certain health behaviors. Twenty eligible studies
were included for analysis. Key Results: The 20 studies addressed various health behaviors: chronic disease
self-management (n = 8), medication adherence (n = 2), overall health status (n = 4), oral care (n = 1), cancer
screening (n = 1), shared decision-making (n = 1), health information sharing (n = 1), physical activity and eat-
ing behaviors (n = 1), and emergency department visits (n = 1). Most studies were conducted in the United
States (n = 13) and used a cross-sectional design (n = 15). The Short Test of Functional Health Literacy in Adults
was commonly used to assess health literacy levels. Selection of variables and their operationalization were
informed by a theoretical model in 12 studies. Age, gender, race/ethnicity, and insurance status were reported
antecedents to health literacy. The most commonly tested mediators were self-efficacy (n = 8) and disease
knowledge (n = 4). Fit indices reported in the studies ranged from acceptable to excellent. Discussion: Current
evidence supports self-efficacy as a mediator between health literacy and health behavior. Further research
is needed to identify how health literacy interplays with known psychosocial factors to inform people’s use of
preventive care services. Future studies should include more disadvantaged populations such as immigrants
with high disease burden and those with low health literacy. Theory-based, empirically tested health literacy
models can serve as the conceptual basis for developing effective health interventions to improve health be-
haviors and ultimately decrease the burden of disease in such vulnerable populations. [HLRP: Health Literacy
Research and Practice. 2020;4(1):e21-e44.]
Plain Language Summary: This review systemically compiles, and critically appraises 20 existing studies that
test conceptual frameworks that propose potential pathways through which health literacy affects health
behaviors. The findings from this review can help inform the development of health literacy-focused interven-
tions to improve the health behaviors of populations with disease burdens.
Health literacy (HL) is a multidimensional concept that ad- minority groups often have low HL levels and have been found to
dresses a range of skills people need to effectively and efficiently have poor health outcomes (Crook, Stephens, Pastorek, Mackert,
function in a health care environment (Baker, 2006; Guzys, & Donovan, 2016; Diviani, van den Putte, Giani, & van Weert,
Kenny, Dickson-Swift, & Threlkeld, 2015; Kindig, Panzer, & 2015; Feinberg, Greenberg, & Frijters, 2015).
Nielsen-Bohlman, 2004). People of older age and those who be- There has been a proliferation of studies on the impact of HL
long to low-income, low-education, immigrant, and ethnic/racial on health behavior (e.g., self-care, chronic disease management)
HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020 e21
and overall health outcomes (Guzys et al., 2015; Kim & Han, framework. Searches were not limited to a specific year.
2016; Oldach & Katz, 2014). These studies discuss the direct re- With the assistance of a health science librarian, we identi-
lationship between HL and health behaviors or health outcomes fied and used the following keywords and medical subject
at the bivariate level. Recently, a growing body of research has headings in searching the electronic databases for relevant
revealed comprehensive pathways related to HL and health be- studies: “health literacy,” “theoretical models,” and “concep-
haviors or outcomes. For example, psychosocial factors such as tual frameworks” (see Table A for specific search terms that
disease knowledge, self-efficacy, and decisional balance, which were used). Search terms were also truncated and explod-
are known determinants of health behaviors, were affected by ed (i.e., search terms were used to retrieved all references
HL levels, and some studies have identified these psychoso- indexed to that term), and other relevant Boolean opera-
cial factors as potential mediators to the relationship between tors were used to make the search as sensitive as possible.
HL and health behavior (Harvey, Vegesna, Mass, Clarke, & Electronic searches were also supplemented by a search on
Skoufalos, 2014; Hui et al., 2014; Kaufman, Mirkovic, & Chan, Google Scholar, and the reference lists of relevant articles
2017; Kim & Han, 2016; Oldach & Katz, 2014; Tanaka, Strong, were examined for articles that were not indexed by the
Lee, & Juon, 2013). However, what remains unclear is how the- electronic databases. In March 2019, we performed an ad-
ory informs the development of HL conceptual frameworks and ditional database search using the same strategies we used
the methods used to empirically assess the proposed pathways in the initial search.
through which HL influences health behavior (Alper, 2018; Kim
& Han, 2016; Oldach & Katz, 2014; Sørensen et al., 2012). Study Eligibility
It is important to gain a comprehensive understanding of All studies were analyzed for their relevance for the pur-
the theories that guide the systematic application and evalua- pose of our review. Studies that addressed the impact of HL
tion of variables used in addressing HL and health behaviors on a health behavior or health outcome, described and em-
(Alper, 2018). The purpose of this systematic review is to crit- pirically tested a conceptual framework, and were written in
ically appraise studies that tested a theory-based HL concep- English were included in this review. Studies were excluded
tual framework. In addition, we were interested in discussing if they addressed HL as a study concept but did not empiri-
mechanisms through which HL influences health behavior cally test a conceptual framework, did not address the impact
and/or health outcome to build on empirical evidence. of HL on health behavior, and were not published in Eng-
lish. Case studies, qualitative studies, conference abstracts,
METHODS and study protocols and non–peer-reviewed editorial works
Search Strategy were also excluded. For the purposes of this article, we define
In October 2017 we performed searches on the elec- conceptual framework as a product that “graphically or nar-
tronic databases PubMed, Embase, and CINAHL to find ratively explains study variables and the presumed relation-
studies that identify and empirically test a HL conceptual ships among them” (Maxwell, 2013).
Joycelyn Cudjoe, PhD, RN, is a Nurse Research Scientist, Inova Health System. Sabianca Delva, BSN, RN, is a Doctoral Candidate, The Johns Hopkins
University School of Nursing. Mia Cajita, PhD, RN-BC, is an Assistant Professor, University of Illinois at Chicago. Hae-Ra Han, PhD, RN, FAAN, is a Professor,
The Johns Hopkins University School of Nursing.
©2020 Cudjoe, Delva, Cajita, et al.; licensee SLACK Incorporated. This is an Open Access article distributed under the terms of the Creative Commons At-
tribution 4.0 International (https://creativecommons.org/licenses/by/4.0). This license allows users to copy and distribute, to remix, transform, and build
upon the article, for any purpose, even commercially, provided the author is attributed and is not represented as endorsing the use made of the work.
Address correspondence to Joycelyn Cudjoe, PhD, RN, Inova Health System, 8110 Gatehouse Road, Suite 200W, Falls Church, VA 22042; email: joycelyn.
cudjoe@inova.org.
Grant: This research was supported by a National Cancer Institute predoctoral training grant (F31CA221096) to J.C.
Disclaimer: The content is solely the responsibility of the authors.
Acknowledgment: The authors thank Stella Seal, medical librarian (Welch Medical Library, Johns Hopkins University), for her assistance with the
literature search.
Disclosure: The authors have no relevant financial relationships to disclose.
Received: October 15, 2018; Accepted: March 20, 2019.
doi:10.3928/24748307-20191025-01
e22 HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020
Study Selection and Data Extraction items that were not addressed by the authors. Total scores for
Covidence, an Internet-based software platform that each study ranged from 0 to 7, with a higher total score at-
streamlines the production of systematic reviews, was used tributed to higher quality rating. Studies with a total score
in the study selection and data extraction process. Our initial less than 3 were rated as low quality, studies with total scores
database search yielded a total of 900 studies, of which 169 ranging from 3 to 4 were rated as medium quality, and stud-
duplicates were removed. To enhance the rigor of the system- ies with total scores of 5 or higher were rated as high quality.
atic review process, two authors (J.C. and S.D.) independently Findings from the quality assessments were used to critique
screened all abstracts and titles for relevance to empirical the overall methodological strengths and weaknesses of the
testing of HL models and frameworks. All conflicts and dis- studies
crepancies were discussed and resolved through face-to-face Results of the quality assessment process are shown in
group discussions. A total of 676 articles were excluded for Table 1. All of the studies adequately described inclusion cri-
nonrelevance to our study’s purpose. The full texts of 55 rele- teria and the characteristics of study participants. There was
vant abstracts were then reviewed independently by the study adequate discussion of items addressing selection bias in most
authors (J.C., S.D., M.C., and H.H.) using the study’s inclu- studies included in the review: description of inclusion crite-
sion and exclusion criteria. We excluded 39 studies for the ria (n = 19), and description of study characteristics (n = 15).
following reasons: (a) studies did not include or propose an Most studies included in the review inadequately addressed
HL framework (n = 27); (b) no empirical data were presented measurement bias: identification of confounders (n = 8), use
(n = 6); (c) studies did not address the impact of HL on health of valid and reliable measurement of outcome (n = 6), and
behavior (n = 3); (d) studies do not include HL as a study vari- strategy addressing confounders (n = 8). The measurement
able (n = 1), (e) no full text was available (n = 1); and (f) it was of outcomes in more than 75% (n = 15) of studies was based
a podium presentation (n = 1). Using the same search terms on self-reports. Overall, most studies had high (n = 10) to
(Table A), an additional database search was conducted in medium (n = 6) quality ratings. Only four studies received a
March 2019 for studies published since November 2018. Af- low-quality rating.
ter removing duplicates, 90 titles with abstracts were reviewed
for relevance. Two study authors (J.C. and S.D.) independently RESULTS
reviewed 17 full texts using the study’s inclusion and exclusion Overview of Studies Included
criteria. A total of 13 articles were excluded for the following The characteristics of all 20 studies included in this review
reasons: (a) studies did not propose a HL framework (n = 9); are detailed in Table 2. Most of the studies were published
(b); studies did not address the impact of HL on health be- in the United States (n = 13) (Brega et al., 2012; Chen, 2014;
havior (n = 2); (c) studies were not written in English (n = 1); Cho, Lee, Arozullah, & Crittenden, 2008; Como, 2018; Crook
and (d) no empirical data were presented (n = 1). Figure 1 et al., 2016; Guo et al., 2014; Hickman, Clochesy, & Alaamri,
provides a detailed description of the selection process. Two 2016; Jin, Lee, & Dia, 2019; Osborn, Cavanaugh, et al., 2011;
study authors (J.C. and S.D.) extracted data from a total of 20 Osborn, Cavanaugh, Wallston, & Rothman, 2010; Osborn,
studies for this systematic review. To enhance interrater reli- Paasche-Orlow, Bailey, & Wolf, 2011; Schillinger, Barton, Kar-
ability and the accuracy of information presented, the authors ter, Wang, & Adler, 2006; Soones et al., 2017), with the remain-
compared key findings and other relevant data, and discrep- ing studies published in China (n = 2) (Sun et al., 2013; Zou,
ancies were resolved. Chen, Fang, Zhang, & Fan, 2017), Taiwan (n = 2) (Hou et al.,
2018; Y. J. Lee et al., 2016), Thailand (n = 2) (Intarakamhang
Quality Assessment & Intarakamhang, 2017; Photharos, Wacharasin, &
The Joanna Briggs Checklist was the appraisal tool used in Duongpaeng, 2018), and South Korea (n = 1) (E. H. Lee,
the quality assessment of all studies included in this review Lee, & Moon, 2016). Study designs included cross-sectional
(Joanna Briggs Institute, 2018). The checklist is a series of (n = 19) (Brega et al., 2012; Chen, 2014; Cho et al., 2008; Como,
questions that authors of observational studies are expected 2018; Crook et al., 2016; Guo et al., 2014; Hickman et al., 2016;
to answer to enhance a study’s methodological rigor. Spe- Hou et al., 2018; Jin et al., 2019; E. H. Lee et al., 2016; Y. J. Lee et
cifically, each study’s quality was assessed using seven items al., 2016; Osborn, Cavanaugh, et al., 2011; Osborn et al., 2010;
addressing selection bias, measurement bias, confounding Osborn, Paasche-Orlow, et al., 2011; Photharos et al., 2018;
variables, and appropriate use of statistical analyses (Joanna Schillinger et al., 2006; Soones et al., 2017; Sun et al., 2013; Zou
Briggs Institute, 2018). Studies were assigned a score of 1 for et al., 2017) and mixed methods (n = 1) (Intarakamhang &
items that were adequately described, and a score of 0 for Intarakamhang, 2017). Sample sizes ranged from 62 to 2,594,
Figure 1. Study selection process. HL = health literacy.
with only seven studies calculating sample sizes a priori Cavanaugh, et al., 2011; Osborn et al., 2010; Sun et al., 2013),
(Chen, 2014; Como, 2018; Hou et al., 2018; Intarakamhang & four studies measured numeracy (Brega et al., 2012; Como,
Intarakamhang, 2017; E. H. Lee et al., 2016; Y. J. Lee et al., 2018; Crook et al., 2016; Soones et al., 2017), and four stud-
2016; Photharos et al., 2018). ies measured functional literacy (Hou et al., 2018; Osborn,
Study participants in all the U.S.-based studies were pre- Paasche-Orlow, et al., 2011; Photharos et al., 2018; Schillinger
dominately female, urban dwellers, adults (age range, 18-75 et al., 2006). Three studies addressed disease-specific HL: dia-
years) with less than a high school education. In addition, the betes (Osborn, Cavanaugh, et al., 2011; Osborn et al., 2010)
samples in U.S.-based studies were more than 50% ethnic/ra- and heart failure (Zou et al., 2017). All studies used an exist-
cial minority groups (i.e., Black, Hispanic, Native American/ ing and well-validated HL measure except one study in Thai-
Alaska Native) except for three studies that included more than land that developed and validated the Health Literacy Scale
60% White participants (Chen, 2014; Guo et al., 2014; Osborn, for Thai overweight children (Chronbach’s alpha: 0.70) (Inta-
Cavanaugh, et al., 2011). One U.S.-based study (Crook et al., rakamhang & Intarakamhang, 2017). The most common HL
2016), however, did not report the race or ethnicity of study measures were the Rapid Estimate of Adult Literacy in Medi-
participants. All studies in this systematic review included cine (REALM) (Osborn, Cavanaugh, et al., 2011; Osborn et
adult participants (age >18 years) except for one study in Thai- al., 2010), Short Test of Functional Health Literacy in Adults
land that used national data from school-age children between (S-TOFHLA) (Cho et al., 2008; Como, 2018; Soones et al.,
ages 9 and 14 years (Intarakamhang & Intarakamhang, 2017). 2017), and Test of Functional Health Literacy in Adults (TOF-
All studies measured one or more subdimensions of HL. HLA) (Osborn, Paasche-Orlow, et al., 2011; Schillinger et
Eight studies measured print literacy (Brega et al., 2012; Chen, al., 2006). Additional measures included the Health Literacy
2014; Cho et al., 2008; Como, 2018; Jin et al., 2019; Osborn, Scale, Brief Health Literacy Tool, the Mandarin version of
TABLE 1
Quality Assessments of Studies
Standard Strategies for

Description Criteria Used for Addressing Valid and Reliable
Description of of Study Measurement of Identification of Confounding Measurement of Statistical Overall
Reference Inclusion Criteria Characteristic the Condition Confounders Factors Outcome Analyses Quality
Brega et al. (2012) 1 1 1 1 1 1 1 High
Chen et al. (2014) 1 1 1 0 0 0 1 Medium
Cho, Lee, Arozullah, & Crittenden (2008) 1 1 0 0 0 0 1 Medium
Como (2018) 1 1 1 1 1 1 0 High
Crook, Stephens, Pastorek, Mackert, & 1 0 0 0 0 0 1 Low
Donovan (2016)
Hou et al. (2014) 1 0 0 1 1 0 1 Medium
HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Hickman, Clochesy, & Alaamri (2016) 1 1 1 0 0 0 0 Medium
Huo et al. (2018) 1 1 1 0 0 1 1 High
Intarakamhang & Intarakamhang (2017) 1 0 0 0 0 0 1 Low
Jin, Lee, & Dia (2019) 1 1 0 1 1 0 1 High
E.H. Lee, Lee, & Moon (2016) 1 1 0 0 0 0 1 Medium
Y.J. Lee et al. (2016) 1 1 1 0 0 1 1 High
Osborn, Cavanaugh, et al. (2011) 0 0 0 0 0 0 1 Low
Osborn, Cavanaugh, Wallston, & 1 1 1 0 0 1 1 High
Rothman (2010)
Osborn, Paasche-Orlow, Bailey, & Wolf 1 1 0 0 0 0 1 Medium
(2011)
Photharos, Wacharasin, & Duongpaeng 1 0 1 0 0 0 0 Low
(2018)
Schillinger, Barton, Karter, Wang, & Adler 1 1 1 1 1 1 1 High
(2006)
Soones et al. (2017) 1 1 1 1 1 0 0 High
Sun et al. (2013) 1 1 0 1 1 0 1 High
Zou, Chen, Fang, Zhang, & Fan (2017) 1 1 0 1 1 0 1 High
Note. 1 = clearly discussed; 0 = not discussed.
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TABLE 2
Study Characteristics and Main Findings

Reference Study Purpose Setting/Sample HL Domains (HL Measure) Main Results
Brega et al. (2012) To develop a theoretical framework 2,594 rural-dwelling adults with diabetes Print literacy (TOHFLA) High HL associated with decreased HbA1c
and test the mechanisms through Country: United States Numeracy (not stated) levels (B = –0.070, p < .05). Significant associa-
which HL is associated with outcomes, tion between high HL and healthy behaviors
Age: 18-65 y; Income: <$10,000; 93% less
focusing on the relationship between (frequent healthy diet, monitor blood sugar).
than college graduates
HL and glycemic control among Self-monitoring of blood sugar mediates HL and
Ethnicity: 100% Native American and
Native Americans and Alaska Natives glycemic control (B = –0.028, p < .05). Diabetes
Alaska Native
with diabetes knowledge is a significant mediator between HL
HL levels: not stated and glycemic control
(beta = –0.134, p < .05)
Chen et al. (2014) Test a model to explain the relation- 63 urban-dwelling adults with heart failure Print literacy (s-TOHFLA) Direct relationship between HL and heart failure
ships between HL, heart failure knowl- Country: United States knowledge (beta = 0.46, p < .05). Heart failure
edge, self-efficacy, and self-care knowledge and self-efficacy do not mediate
Mean age: 62.1 y; mean years of educa-
the relationship between HL and heart failure
tion: 13.7 y; female: 47.6%
self-care
Ethnicity: 86% White; 11% Black, 2%
Hispanic/Latino, 2% Native American/
Alaska Native
HL levels: inadequate 16%, marginal 16%,
adequate, 68%
Cho, Lee, Arozullah, Explore intermediate factors that link 489 urban-dwelling adults with Medicare Print literacy/compre- Positive, direct relationships between HL, health
& Crittenden (2008) HL to health status and use of health Country: United States hension (s-TOFHLA) status (beta = 0.48, p < .05); direct negative
services (ED visit, hospitalization) relationship between HL and hospitalization and
Age: >65 y
ED visits respectively (beta = –0.24 and beta =
Average education level: HS graduate;
–0.35). Compliance and disease knowledge are
female: 78.7%
not significant mediators between HL and out-
Ethnicity: 59.1% Black comes (health status, hospitalizations, ED visit). HL
HL levels: inadequate 51% mediates educational attainment and outcomes
(health status, hospitalization and ED visits)

TABLE 2 (continued)

Como (2018) Investigate whether HL, self-efficacy, 175 urban-dwelling adults diagnosed with Print literacy/compre- Self-efficacy is associated with physical health
and medication adherence can ex- heart failure and attending cardiology hension (s-TOFHLA) status (p = .002). Education, income, marital sta-
plain or predict the variance in health health centers in New York, NY Numeracy (s-TOFHLA) tus (widow), illness severity indicators (number
outcomes (perceived physical or Country: United States of medication/days, frequency/day) are signifi-
mental health status) in persons with cant predictors of physical health status
Mean age: 73 y; male: 66.9%
chronic heart failure (p < .001). No associations between HL, medica-
Ethnicity: 11.4% Black, 83.4% White, 4%
tion adherence, and physical health status.
Hispanic/Latino, 0.6% Asian, 0.6% Native
Medication adherence does not mediate the
American
relationship between HL and physical health sta-
HL levels: inadequate 38.3%, tus. Medication adherence (p < .001), numeracy
adequate: 45.7% (p = .029), and reading comprehension (p = .049)
are associated with mental health status. Medi-
cation adherence does not mediate the relation-

ship between HL and mental health status
Crook, Stephens, Explain the associations among per- 180 English-speaking adults recruited Numeracy (Newest Vital Internet use positively associated with HL
Pastorek, Mackert, ceived health knowledge, information from a central Texas acute and preventive Sign) level (beta = 0.55, p < .001). Attitude toward
& Donovan (2016) sharing, attitudes, behaviors, and HL care center information mediates relationship between HL
Country: United States and behavioral intention (p < .001) as well as
the relationship between HL and information
Age: 18-75 y; mean age 38.7 y +13.2;
sharing (p < .001). No significant association be-
female: 69%
tween perceived healthy heart knowledge and
Education: not reported
HL (beta = 0.14, p = .14). High perceived healthy
Ethnicity: not reported heart knowledge associated with positive at-
HL levels: not stated titudes toward health information (beta = 0.13,
p = .03) and lower perception of information
overload (beta = –0.14, p = .01)
Guo et al. (2014) Examine effects of HL, patient-dentist 1,799 rural-dwelling adults in Florida Navigation (Chew’s Significant direct association between HL and
communication, dental care patterns Country: United States 3-Item HL scale) self-rated oral health (beta = 0.091, p < .001).
on self-rated oral health status Patient-dentist communication and dental care
Mean age: 52.9 y; HS graduate or lower:
patterns mediate the relationship between HL
53%; female: 53%; Ethnicity: 34% Black,
and self-rated oral health (beta = 0.003, p = .01)
66% White
HL levels: low 31%, high 69%
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TABLE 2 (continued)

Hickman, Clochesy, Examine predictive associations 109 English-speaking, urban-dwelling Functional (Chew’s HL (beta = 0.15, p < .10), quality of provider
& Alaamri (2016) among HL, quality of the provider adults with hypertension in Northeast 1-item scale) interaction (beta = 0.38, p < .01), perceived com-
interaction, perceived communication Ohio munication skills (beta = 0.22, p < .05) directly
skills, and behavioral activation on Country: United States associated with behavioral activation. Provider
blood pressure control interaction (beta = 0.27, p < .001) and behavioral
Mean age: 52 y (±11); education: not
activation (beta = –0.29, p < .001) are directly
reported; Female: 59%; Income: not
associated with blood pressure control
reported
Ethnicity: 68% Black, 24% White, 5%
Hispanic, 3% Multiracial
HL levels: not stated
Hou et al. (2018) To examine the mechanisms and 511 adults diagnosed with breast cancer Functional, comprehen- Age and cancer stage are inversely related to HL
completeness of the Integrated Model and attending breast surgery clinics and sion (p < .05). Education (beta = 0.41, p < .05), cancer
of HL teaching hospitals (Mandarin version of duration (beta = 0.27, p < .05) significantly as-
Country: Taiwan HLS-EU-Q) sociated with HL
Mean age: 57.9 y; <HS graduate: 31.7%; Significant associations among patients’ par-
Married: 71.6%; residence: 75% urban ticipation in shared decision-making
dwellers; employment: 44% unemployed; (beta = 0.46, p < .05), self-rated health status
average duration of cancer diagnosis: 43 (beta = 0.27, p < .05) and HL
months No associations among marital status, place of
HL levels: inadequate: 37.5%; adequate: residence, occupation, and HL
62.5%
Intarakamhang & Develop a scale for evaluating HL level 2,000 population-based sample of urban Media, functional, navi- Direct effect of critical skills (media literacy and
Intarakamhang of overweight children in Thailand and provincial Thai students gation (HL scale for over- making appropriate health-related decision) on
(2017) and develop a model of health behav- Country: Thailand weight Thai childrena) obesity preventive behaviors (eating, exercise
ior to prevent obesity and emotional behaviors) (beta = 0.55, p < .05)
Age: 9-14 y; education: not reported; sex:
not reported; income: not reported Basic intelligence skills (health knowledge,
accessing information and services) directly
Ethnicity: 100% Asian
related to interactive skills (communication and
managing health conditions) (beta = 0.76,
p < .05)
Direct relationship between interactive skills and
critical skills (beta = 0.97, p < .05)

TABLE 2 (continued)

Jin, Lee, & Dia (2019) Examine hypothetical pathways 433 Korean American adults living in the Print literacy, compre- Online health information seeking behaviors
through which online health southeastern United States hension (Brief HL Screen- associated with HL (beta = 0.146, p < .001) and
information-seeking behaviors (using Country: United States ing Tool) information overload (beta = 0.179, p < .01)
emails to communicate with provid- Information overload inversely associated with
Mean age: 57.6 y, female: 60.8%; family
ers, visit social networking site to read HL (beta = –0.242, p < .001). Decisional balance
history of cancer: 54.6%; no personal
and share medical topics) influence associated with HL (beta = 0.124, p < .05), fecal
history of cancer: 85.4%; education: not
HL, which, in turn, leads to colorectal occult blood test (beta = 0.161, p < .05) and
reported
cancer screening among Korean sigmoidoscopy uptake (beta = 0.169, p < .01)
Americans HL not significantly associated with fecal occult
blood test, sigmoidoscopy, and colonoscopy
uptake
HL does not mediate the relationship between
online information seeking and colorectal
cancer screening

E.H. Lee, Lee, & Explore the relationships among HL, 459 Korean-speaking adults diagnosed Functional (communica- Direct effect of HL on self-efficacy (beta = 0.45,
Moon (2016) self-efficacy, self-care activities, and with type 2 diabetes, recruited from uni- tion) (Health Literacy p < .001) and self-care activities (beta = .209,
HRQOL versity hospitals in South Korea between Scale) p < .001). Self-efficacy mediates relationship
2014 and 2015 between HL and self-care activities (beta =
Country: South Korea 0.299, p = .005). Self-care activities are directly
related to HRQOL (beta = 0.399, p < .001). No
Age: 20-70 y; mean age 59.6 y (±10.57);
direct effect of HL on HRQOL. Self-care activities
female: 60%; less than HS graduate: 32%;
mediate relationship between HL and HRQOL
income: not reported
(beta = 0.203, p = .002). Self-care activities
mediate relationship between self-efficacy and
HRQOL (beta = 0.265, p = 0.004)
Y.J. Lee et al. (2016) Validate a hypothesized model 295 person convenience sample of adult Functional (communica- Nonsignificant association between age and
exploring the influencing pathways of patients diagnosed with type 2 diabetes tion) (Health Literacy HL, HL and self-care behaviors, empowerment
empowerment perceptions, HL, self- >6 months and attending endocrine out- Scale) and self-efficacy, empowerment and self-care
efficacy, and self-care to HbA1c levels patient clinics in southern Taiwan behaviors.
among patients with type 2 diabetes Country: Taiwan HL mediates relationship between empower-
Age: 20-80 y; mean age: 58.2 y; ment and self-efficacy (beta = 0.39, p < .001).
female: 42%; less than HS graduate: 37.3%; Self-efficacy and HL also mediate the relation-
Income: 68% low SES ship between self-care behaviors and empower-
ment (beta = 0.26, p < .001).
Self-care behaviors mediates self-efficacy and
glycemic control (beta = –.14; p < .05)
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TABLE 2 (continued)

Osborn, Cavanaugh, Test whether HL and/or numeracy 383 English -peaking urban, rural, and Diabetes-related HL does not mediate relationship between Black
et al. (2011) are related to diabetes medication suburban dwelling adults living in North numeracy (Diabetes race and diabetes medication adherence. Direct
adherence and whether either factor Carolina and Tennessee diagnosed with Numeracy Test) negative association between Black race and HL
explained racial differences in adher- types 1 and 2 diabetes Print literacy (REALM) (beta = –0.28, p < .001). Non-significant associa-
ence to diabetes medications Country: United States tion between HL and medication adherence
(p = .06). Direct association between duration
Age: 18-85 y; Mean age: 54 y; female: 50%;
of diabetes and medication adherence (beta =
<HS graduate: 44%; income >$20,000:
0.13, p < .01)
56%
Ethnicity: 35% Black
Osborn, Cavanaugh, Examine the predicted pathway 383 English-speaking urban, rural, and Diabetes-related Younger age (p < .001), insulin use (p < .001),
Wallston, & linking HL, numeracy, and diabetes suburban dwelling adults living in North numeracy (Diabetes increased duration of diabetes diagnosis (p < .01),
Rothman (2010) self-efficacy to glycemic control Carolina and Tennessee diagnosed with Numeracy Test) Black race (p < .01) are directly associated with
Types 1 and 2 diabetes Print literacy (REALM) higher HbA1c levels. Greater self-efficacy associ-
Country: United States ated with lower HbA1c levels (r = –0.25, p < .001).
Model accounted for 21% variability in HbA1c.
Age: 18-85 y; mean age: 54 y; female: 50%;
No direct relationship between HL and glycemic
>HS education: 56%; income >$20,000:
control (HbA1c). Self-efficacy mediates relationship
56%
between general numeracy and glycemic control
(p < 0.05)
Osborn, Paasche- Validate the Paasche-Orlow and Wolf 330 English-speaking adults with hyper- Functional literacy Low education (beta = 0.56, p< .001), Black race
Orlow, Bailey, & Wolf model examining mechanisms linking tension recruited from clinics across the (s-TOFHLA) (beta = 0.51, p < .001), older age (beta = 0.36,
(2011) HL to physical activity and self- United States. p < .001) directly associated with low HL. High HL
reported health status Country: United States associated with high knowledge (beta = 0.22,
p < .001). Self-efficacy directly related with
Mean age: 53.6 y; female: 68%; <HS educa-
health status (beta = 0.17, p < .01). No asso-
tion: 70.7%; unemployed: 66%; uninsured:
ciation between self-care behavior and health
44%
status. Nonsignificant relationship between
race and self-efficacy (beta = 0.10). Knowledge
HL levels: not stated mediates relationship between HL and self-
efficacy (B = 0.045, p < .001)

TABLE 2 (continued)

Photharos, Wachara- Develop and test the causal relation- 275 adults experiencing early stage Functional, communi- HL (beta = 0.31, p < .0), family functioning
sin, & Duongpaeng ships among family functioning, HL, chronic kidney disease and receiving cation, critical literacy (beta = 0.53, p < .05) directly associated with
(2018) chronic kidney disease self-efficacy, medical treatment (Health Literacy Scale) chronic kidney disease self-efficacy
illness perceptions, social support,and Country: Thailand HL (beta = 0.37, p < .05), social support
self-management behaviors among (beta = 0.24, p < 0.05) directly associated with
60% male; college educated: <68%; family
persons experiencing early stages of self-management behaviors
history of chronic kidney disease: 19%;
chronic kidney disease history of hypertension: 36.7%; history of Family functioning is related to self-management
diabetes and hypertension: 29.5% behaviors through social support (beta = 0.15,
HL levels: not stated p < .05)
Chronic kidney disease self-efficacy does not
mediate the relationships among HL, family
functioning, and self-management behaviors

Schillinger, Barton, Explore the pathway linking HL, edu- 395 adults with diabetes recruited from Functional literacy Direct relationship between educational attain-
Karter, Wang, & cation, and glycemic control primary care clinics between June and (s-TOFHLA) ment and HL: HS (beta = 0.24, p < .05), some
Adler (2006) December 2000 in San Francisco, CA college (beta = 0.51, p < .05). Direct association
Country: United States between educational attainment and glycemic
control: HS (beta = –0.11, p < .05), some college
Mean age: 57.9 y; uninsured: 30.6%; prima-
(beta = –0.06, p < .05). HL mediates relationship
ry English speakers: 51.7%; <HS graduate:
between educational attainment (HS education
46.8%; Income <$10,000: 68.8%
(beta = –0.04, p < .05) and some college educa-
Ethnicity: 18.5% Asian/Pacific Islander,
tion (beta = –0.08, p < .05) and glycemic control
25.3% Black, 13.9% White, 42.3% Hispanic
Soones et al. (2017) Describe causal pathway linking HL to 433 older adults with asthma recruited Comprehension and Concerns about medication associated with low
medication adherence from hospital and community practices in numeracy (s-TOFHLA) HL (beta = –0.154, p <.001) and lower medica-
New York and Chicago tion adherence (beta = –0.2, p < .004). Low HL
Country: United States associated with low medication adherence
through medication concerns (beta = 0.033,
Age: 60-70 y; mean age: 67 y;
p = .002). Direct relationship between HL and
female: 84%, <HS graduate: 32.6%; Income
medication adherence (beta = 0.123, p < .001).
<$1,350/month: 54%
Cognition directly associated with HL
Ethnicity: 31% Black, 39% Hispanic
(beta = –0.767, p < .001). Nonsignificant relation-
HL levels: adequate: 64%; limited: 36% ships between HL and medication necessity and
illness beliefs and medication adherence
e31
e32
TABLE 2 (continued)

Sun et al. (2013) Develop and validate a HL model to 3,222 city-dwelling Chinese adult Print literacy, numeracy Education has positive and direct effect on prior
explain the determinants of HL and residents (Skill-based HL tool)a knowledge of infectious respiratory diseases
the associations between HL and Country: China (beta = 0.324, p < .01) and HL (beta = 0.346)
health behaviors HL directly related to health behavior (beta =
Age: 16-81 y; mean age: 33.8 y; <HS gradu-
ate: 38.4%; income <3,000 Yuan (~$438): 0.101). Age directly associated with health status
83.2% (beta = 0.107)
Zou, Chen, Fang, Explore factors associated with self- 321 adults with chronic heart failure Functional Literacy (Chi- Functional capacity (beta = 0.155, p < .01) and
Zhang, & Fan (2017) care behaviors and examine mediat- recruited from cardiovascular units in nese version of Health knowledge (beta = 0.321, p < .01) directly associ-
ing role of self-care confidence Shandong, China Literacy Scale for patients ated with self-care management. HL (beta = 0.043,
Country: China with Chronic Disease) p < .01) and social support (beta = 0.146, p < .01)
are directly associated with self-care maintenance.
Mean age: 64 y; female: 49%; <HS gradu-
Self-care confidence is directly associated with
ate: 65.1%; unemployed: 59.2%; income
both self-care maintenance (beta = 0.123, p < .05)
<1,000 Yuan (~$155): 27.4%
and management (beta = .309, p < .01). Age
(beta = 0.194, p < .01) and health failure duration
HL levels: not stated (beta = 0.105, p < .05) are significantly associated
with self-care maintenance. Self-care confidence
mediates relationships between knowledge
(beta = 0.0225, p < .01), HL (B = 0.162, p < .01),
social support (beta = 0.174, p < .01), and self-care
behaviors
Note. Design of all the studies was cross-sectional except for the study by Intarakamhang & Intarakamhang (2017), which used mixed methods. ED = emergency department; HbA1c = hemoglobin A1C; HL = health literacy; HLS-EU-Q: European Health
Literacy Survey Questionnaire; HRQOL = health-related quality of life; HS = high school; REALM = Rapid Estimate of Adult Literacy in Medicine; SES = socioeconomic status; S-TOFHLA = Short Test of Functional Health Literacy in Adults; TOFHLA = Test of
Functional Health Literacy in Adults.
a
Health literacy instrument designed for purposes of the study.

the European Health Literacy Survey Questionnaire, and the 2016; Osborn et al., 2010; Osborn, Paasche-Orlow, et al., 2011;
Chinese Version of Health Literacy Scale for Patients with Photharos et al., 2018; Schillinger et al., 2006; Zou et al., 2017),
Chronic Disease (E. H. Lee et al., 2016; Y. J. Lee et al., 2016; colorectal cancer screening (n = 1) (Jin et al., 2019), medication
Zou et al., 2017), which were mostly used in international adherence (n = 2) (Osborn, Cavanaugh, et al., 2011; Soones et
studies (Taiwan, South Korea, Thailand, and China) to assess al., 2017), overall health status (n = 4) (Como, 2018; Hou et al.,
functional HL in the context of breast cancer, chronic kidney 2018; E. H. Lee et al., 2016; Sun et al., 2013), oral care (n = 1)
disease, diabetes, and heart failure management. Similarly, (Guo et al., 2014), health information sharing (n = 1) (Crook
two studies (Guo et al., 2014; Hickman et al., 2016) conduct- et al., 2016), physical activity and eating behaviors (n = 1)
ed in the U.S. across ethnically diverse samples (predomi- (Intarakamhang & Intarakamhang, 2017), shared decision-
nantly Black, non-Hispanic middle-aged women) assessed making in relation to breast cancer care (n = 1) (Hou et al.,
functional literacy using Chew’s 3-item scale and 1-item scale 2018), and emergency department visits (n = 1) (Cho et al.,
(Chew et al., 2008). 2008). These studies reported that HL leads to better self-care
and medication adherence, improved health status, improved
Antecedents and Outcomes of HL self-reported oral health, less frequent emergency depart-
Table 3 details the antecedents, mediators, moderators, and ment visits, shorter hospitalizations, and improved physical
outcomes of HL as outlined in the studies. All but four studies activity and healthy eating behaviors (Brega et al., 2012; Cho
identified demographics and psychosocial factors as the most et al., 2008; Guo et al., 2014; Hou et al., 2018; Intarakamhang
common antecedent to HL (Hickman et al., 2016; Osborn et & Intarakamhang, 2017; Soones et al., 2017; Sun et al., 2013;
al., 2010; Photharos et al., 2018; Zou et al., 2017). The authors Zou et al., 2017). However, HL did not affect information-
reported the following sociodemographic and medical charac- sharing behaviors (Crook et al., 2016), patients’ participation
teristics: age, education, income, health insurance status, race/ in shared decision-making (Hou et al., 2018), and colorectal
ethnicity (Brega et al., 2012; Chen, 2014; Cho et al., 2008; Como, cancer screening (Jin et al., 2019). Six studies did not find a sig-
2018; Guo et al., 2014; Hou et al., 2018; Osborn, Paasche-Orlow, nificant association between HL and reported health behaviors
et al., 2011; Schillinger et al., 2006), general literacy and language (physical activity, medication adherence, glycemic control) or
(English proficiency) (Schillinger et al., 2006), marital status health outcomes (self-rated health of patients with diabetes
(Como, 2018; Y. J. Lee et al., 2016), Internet use (Crook et al., and chronic heart failure) (Como, 2018; Y. J. Lee et al., 2016;
2016; Jin et al., 2019), disease duration (Y. J. Lee et al., 2016), Osborn, Cavanaugh, et al., 2011; Osborn et al., 2010; Osborn,
and cognition (Soones et al., 2017). Older age (Hou et al., 2018; Paasche-Orlow, et al., 2011; Schillinger et al., 2006).
Osborn, Paasche-Orlow, et al., 2011), low education (Os-
born, Paasche-Orlow, et al., 2011), and Black race (Osborn, Pathways Linking HL and Health Behaviors/Outcomes
Cavanaugh, et al., 2011; Osborn, Paasche-Orlow, et al., 2011) All but three studies assessed a number of variables as
were linked to low HL, whereas increased years of education possible mediators between HL and health behaviors/out-
(Schillinger et al., 2006; Sun et al., 2013) and Internet use (Crook comes (Hou et al., 2018; Intarakamhang & Intarakamhang,
et al., 2016; Jin et al., 2019) were linked to high HL; however, 2017; Schillinger et al., 2006). Eight studies examined the
a study conducted in China with a sample of older adults with mediating effect of self-efficacy on the relationship between
low-income (N = 295, mean age of 58 years) reported no asso- HL and diabetes management, heart failure management,
ciation between age and HL (Y. J. Lee et al., 2016). Psychosocial and general self-care (Como, 2018; Chen, 2014; E. H. Lee et
antecedents included perceived health knowledge and perceived al., 2016; Y. J. Lee et al., 2016; Osborn et al., 2010; Osborn,
knowledge (Crook et al., 2016; Y. J. Lee et al., 2016; Sun et al., Paasche-Orlow et al., 2011; Photharos et al., 2018; Zou et
2013). A statistically significant association was reported among al., 2017). Of the five studies that measured disease-specific
perceived empowerment, prior knowledge, and HL (Y. J. Lee et (diabetes, heart failure, chronic kidney disease) self-efficacy
al., 2016; Sun et al., 2013). One study among a sample of pre- (E. H. Lee et al., 2016; Y. J. Lee et al., 2016; Osborn et al.,
dominantly middle-aged (mean age, 38 years) women (69%) 2010; Photharos et al., 2018; Zou et al., 2017), four stud-
reported a nonstatistically significant association between per- ies found self-efficacy as a statistically significant mediator
ceived heart health knowledge and HL (Crook et al., 2016). The (E. H. Lee et al., 2016; Y. J. Lee et al., 2016; Osborn et al.,
lack of association can be attributed to potential selection bias. 2010; Zou et al., 2017). However, only two studies (E. H.
Studies addressed the following health behaviors and health Lee et al., 2016; Y. J. Lee et al., 2016) controlled for possible
outcomes: chronic disease self-management (n = 9) (Brega demographic confounders (age, gender, education, marital
et al., 2012; Chen, 2014; Hickman et al., 2016; Y. J. Lee et al., status).
e34
TABLE 3
Theoretical Frameworks of Health Literacy
How Framework Proposed Health Behaviors/ Fit Indices for Final

Reference Was Informed Antecedents to HL Proposed Mediators and Moderators Hypothesis Tested Outcomes Models
Brega et al. Not stated Age, gender, Mediators: diabetes knowledge; Diabetes-related knowledge and Glycemic control X2 = 976.78, df = 255
(2012) income, education behavior (healthy and unhealthy behavior (healthy diet, physical (p not reported)
food consumption, physical activity, activity, self-monitoring of blood CFI: 0.85
self-monitoring blood glucose) sugar) mediate relationship be- RMSEA: 0.03
Moderators: none tween HL and glycemic control Acceptable fit
Chen et al. Orem’s theory Years of formal Mediators: knowledge; self-efficacy Formal education is associated Heart failure self- X2 = 3.05, df = 4
(2014) of self-care; education Moderators: none with HL and has a direct effect on care (maintenance (p = .55)
Bandura’s social heart failure knowledge. Direct and management) CFI: 1
cognitive theory relationship among HL, health RMSEA: 0
failure knowledge, and self-efficacy. GFI: 0.98
Heart failure knowledge mediates NFI: 0.95
relationship between HL and self-
Good model fit
efficacy. Heart failure knowledge
and self-efficacy mediate the rela-
tionship between HL and self-care
Cho, Lee, Not stated Gender, race and Mediators: disease knowledge; Mediating factors (disease knowl- Health status, hospi- X2 = 15.26, df = 13
Arozullah, & education health behavior; preventive care; edge, health behavior, preventive talization, ED visit (p = .29)
Crittenden medication compliance care, and compliance with medica- RMSEA: 0
(2008) Moderators: none tion) link HL and outcomes (health AGFI: 0.91
status, health care, ED visit and
NFI: 0.99
hospitalization)
Adequate fit
Como (2018) Paasche-Orlow Patient demograph- Mediators: medication adherence; HL, medication adherence, and self- Health outcomes Not reported
and Wolf causal ics (age, education, self-efficacy efficacy are associated with physical (physical health
pathways linking ethnicity) Moderators: none health status. Medication adher- status, mental
limited health Social factors (em- ence mediates the relationship health status)
literacy to health ployment, income, between HL and physical health
outcomes language, social status. HL, self-efficacy, and medica-
Bandura’s self- support, marital tion adherence are associated with
efficacy theory status) mental health status. Medication
adherence mediates the relation-
Illness severity
ship between HL and mental health
indicators (number
status
of medications/days,
frequency/day)

TABLE 3 (continued)

Crook, Theory of diffu- Perceived health Mediators: information overload; Frequent Internet use is directly Behavioral inten- X2 = 13.00, df = 12
Stephens, sion of knowledge, Internet attitude toward information related to high HL; higher perceived tion, information (p = .37)
Pastorek, innovations use Moderators: none health knowledge is directly related sharing RMSEA: 0.02
Mackert, & to frequent Internet use, high HL, CFI: 1
Donovan positive attitude toward informa-
TLI: 0.99
(2016) tion, and lower perception of
information overload SRMR: 0.06
Higher HL associated with lower Good model fit

levels of information overload and
positive attitudes toward
information
Perceived level of information over-

load negatively predicts attitude
toward information
Intention to share information
positively predicts behavioral inten-
tions; attitude toward information
positively predicts behavioral
intentions and information-sharing
intentions
Attitude toward information medi-
ates relationship between HL and
behavioral intentions, as well as
relationship between perceived
overload and information-sharing
intentions
Guo et al. Not stated Age, gender, race, Mediators: patient-dentist Hypothesis: high HL associated with Self-rated oral X2 = 0.43 (p = .51)
(2014) education, income, communication; dental care pat- better patient-dentist communica- health RMSEA: 0.01
having a regular terns tion, and better communication is CFI: 0.99
dentist Moderators: none in turn associated with increased
Good model fit
likelihood to seek regular dental
care, resulting in better self-rated
oral health
e35
e36
TABLE 3 (continued)

Hickman, Integrated model None Mediators: quality of provider inter- The association between HL and Blood pressure X2 = 1.1, (p = .76)
Cloch- of client health action; perceived communication blood pressure control is mediated control CFI: 1
esy, & Alaamri behavior skills; behavior activation by quality of provider interaction, RMSEA: 0
(2016) Moderators: none perceived communication skills,
SRMR: 0.03
and behavioral activation
TLI: 1.1
Excellent fit
Hou et al. Integrated model Age, education, can- Mediators: none Intercorrelated determinants of Participation in X2 = 55.12, df = 32
(2018) of HL cer stage, time since Moderators: none HL (age, education, cancer stage, shared decision- (p = .007)
diagnosis, marital time since diagnosis, marital status, making RMSEA: 0.04
status, residential residential area, occupation) predict Self-rated health CFI: 0.99
area, occupation patients’ HL and influence the con- status SRMR: 0.03
sequences of HL (participation in
decision-making, self-rated health AIC: –8.88
status). There is direct relationship Good model fit
between determinants and con-
squences of HL
Intarakam- Nutbeam model Health knowledge Mediators: none Direct relationship between basic Obesity preventive X2 = 60.1, df = 12
hang & Inta- Moderators: none health skill (health knowledge behaviors (eating (p = .00)
rakamhang and understanding) and eating behaviors, exercise RMSEA: 0.05
(2017) behaviors. Association between behaviors, and emo- CFI: 0.99
basic health skill (health knowledge tional coping)
AGFI: 0.99
and eating behaviors) is mediated
by interactive skills (communicating PNFI: 0.72
for added skills) and critical skills Good model fit
(making appropriate health-related
decision)
Jin, Lee, & Dia HL skills frame- Online information- Mediators: decisional balance; Online health information-seeking Colorectal cancer Not reported
(2019) work, cognitive seeking behaviors information overload behavior is positively associated screening
mediation model (using emails to Moderators: none with HL
communicate with Online health information-seeking
providers; visit social behavior is associated with informa-
networking site tion overload
to read and share Information overload is inversely
medical topics) associated with HL

TABLE 3 (continued)

HL is positively associated with
colorectal cancer screening
HL is positively associated with
decisional balance
Decisional balance is positively
associated with colorectal cancer
screening
E.H. Lee, Not stated Age, gender, Mediators: self-efficacy; self-care Study aim: test relationship among HRQOL (emotional X2 = 265.79, df = 71
Lee, & Moon education, marital activities HL, self-efficacy, self-care activities, suffering, social RMSEA: 0.07
(2016) status, treatment Moderators: none and HRQOL functioning, adher- CFI: 0.92
regimen (diet/ex- ence to treatment,
GFI: 0.92

ercise, insulin, oral diabetes-specific
hypoglycemic only, symptoms) SRMR: 0.07
oral hypoglycemic NFI: 0.92
& insulin), HbA1c, Good model fit
duration of disease
Y.J. Lee et al. Paasche-Orlow Education, age, Mediators: self-efficacy; self-care Self-care behaviors mediate rela- Glycemic control X2 / df = 1.79
(2016) and Wolf model empowerment behaviors (medication, exercise, tionship between HL and glycemic (HbAIc) RMSEA: 0.052
perceptions diet, blood sugar monitoring, adver- control (i.e., HbA1c) CFI: 0.94
sity prevention) Direct relationships: (1) HL and GFI: 0.95
Moderators: none self-efficacy, (2) HL and glycemic
AGFI: 0.96
control; (3) empowerment and HL,
self-care behaviors, self-efficacy, AIC: 145.25
and glycemic control Acceptable model fit
Osborn, Not stated Race Mediators: none Black race associated with poor Medication X2 = 0.08 (p = 0.78)
Cavanaugh, et Moderators: none medication adherence; numeracy adherence RMSEA: 0.00
al. (2011) associated with medication adher- CFI: 1.00
ence and explains association
Excellent model fit
between race and adherence
e37
e38
TABLE 3 (continued)

Osborn, Not stated None Mediators: diabetes self-efficacy HL is directly related to glycemic Glycemic control X2 = 6.17, (p = 0.41)
Cavanaugh, Moderators: none after controlling for demograph- CFI: 1
Wallston, & ics (age, gender, race, education, RMSEA: 0.01
Rothman income, insulin use, diabetes type,
Excellent model fit
(2010) and years since diagnosis).
Self-efficacy mediates HL and glyce-
mic control
Osborn, Paasche-Orlow Race, education, age Mediators: knowledge; self-efficacy; Patient demographics (race/ethnic- Health status X2 = 6.75, (p = .40)
Paasche- and Wolf model self-care ity, education, age) predict HL (subjective health) RMSEA: 0.01
Orlow, Bailey, Moderators: none HL predicts determinants of self- CFI: 1
& Wolf (2011) care at the patient level (knowledge Excellent model fit
and self-efficacy)
Patient-level determinants of
self-care predict self-care behavior
(physical activity)
Self-care behavior predicts health
status (subjective health)
Photharos, Individual and None Mediators: chronic kidney disease Family functioning, illness per- Self-management X2 / df = 1.63
Wacharasin, & family self- self-efficacy ception, and HL directly affect behaviors (adher- RMSEA: 0.48
Duongpaeng management Moderators: none self-management behaviors and ence to chronic GFI: 0.93
(2018) theory indirectly affect self-management kidney disease
AGFI: 0.9
behaviors through chronic kidney recommendation,
disease self-efficacy self-integration, Acceptable model fit
Family functioning influences self- problem solving,

management behaviors through seeking social sup-
social support port)
Schillinger, Not stated Educational Mediators: none HL mediates the relationship be- Glycemic control X2 = 12.22, df = 31
Barton, Karter, level, age, primary Moderators: none tween education level and glycemic (p = 0.10)
Wang, & Adler language, health control RMSEA < 0.0001
(2006) insurance status CFI: 1
AGFI: 0.99
Good model fit

TABLE 3 (continued)

Soones et al. Not stated Cognition Mediators: illness beliefs; medica- Asthma illness and medication Medication RMSEA: 0.05
(2017) tion concerns; medication necessity beliefs mediate the relationship adherence CFI: 0.93
Moderators: none between HL and medication adher- Adequate fit
ence
Sun et al. Baker, Paasche- Age, education, Mediators: health behavior Prior knowledge influences devel- Health status X2: 10.22, df = 6
(2013) Orlow income, prior knowl- Moderators: none opment of HL skills (p = .1159)
edge of infectious HL has direct effect on health RMSEA: 0.05
respiratory diseases behaviors CFI: 0.1
HL mediates relationship between AGFI: 0.1
prior knowledge and health Good model fit
behavior

Health behavior influences health
status
Zou, Chen, Capability oppor- None Mediators: self-care confidence Capability (functional capacity, Heart failure self- X2 = 14.04, df = 11
Fang, Zhang, tunity motivation Moderators: None knowledge, HL) and opportunity care maintenance (p = .23)
& Fan (2017) and behavior (social support, socioeconomic Heart failure self- RMSEA: 0.029
model status) are associated with behavior care management CFI: 0.99
(self-care maintenance, self-care
Good model fit
management) through motivation
(self-care confidence)
Note. AGFI: Adjusted Goodness of Fit; AIC: Akaike Information Criterion; CFI = Comparative Fit Index; DF = degrees of freedom; ED = emergency department; GFI = Goodness of Fit Index; HbA1c = hemoglobin A1c; HL = health literacy; HRQOL = health-
realted quality of life; NFI = Normed Fit Index; RMSEA = root mean square error of approximation; X2 = chi-square.
e39
Four studies that examined how HL is related to health Validation of Theory-Based Conceptual Frameworks
behavior through disease knowledge found the following: Fourteen studies (Chen, 2014; Crook et al., 2016; Guo et
only one study showed a statistically significant mediat- al., 2014; Hickman et al., 2016; Hou et al., 2018; Intaraka-
ing effect of knowledge in the context of diabetes manage- mhang & Intarakamhang, 2017; E. H. Lee et al., 2016; Y. J.
ment (Brega et al., 2012), and three studies found a direct Lee et al., 2016; Osborn, Cavanaugh, et al., 2011; Osborn,
association between HL and knowledge (Chen, 2015; Cho et Cavanaugh et al., 2010; Osborn, Paasche-Orlow et al., 2011;
al., 2008; Osborn, Paasche-Orlow et al., 2011). All four stud- Schillinger et al., 2006; Sun et al., 2013; Zou et al., 2017) re-
ies that examined the mediating effect of disease knowledge ported good to excellent goodness of fit in which all indi-
did not describe how knowledge instruments were scored, ces were statistically significant; two studies did not report
however. In addition, all four studies had a large propor- fit indices (Como, 2018; Jin et al., 2019). Of the 20 studies
tion (65%-70%) of study participants with a high school included in this review, all but one hypothesized the relation-
education or less (Chen, 2015; Cho et al., 2008; Osborn, ships among proposed study variables (E. H. Lee et al., 2016).
Paasche-Orlow et al., 2011; Zou et al., 2017). Twelve studies used theory to inform the selection and op-
Of the eight studies that examined self-care activities erationalization of study variables (Chen, 2014; Como, 2018;
(medication adherence, physical activity, self-monitoring of Crook et al., 2016; Hickman et al., 2016; Hou et al., 2018;
blood glucose, foot care, healthy diet) as factors linking the Intarakamhang & Intarakamhang, 2017; Jin et al., 2019; Y. J.
pathway between HL and health outcomes (glycemic control, Lee et al., 2016; Osborn, Paasche-Orlow, et al., 2011; Photharos
emergency department visits, blood pressure control, and et al., 2018; Sun et al., 2013; Zou et al., 2017). Three stud-
physical and mental health status) (Brega et al., 2012; Cho ies validated the theory by Paasche-Orlow and Wolf (2007)
et al., 2008; Como, 2018; Hickman et al., 2016; E. H. Lee et across a sample of low-income, middle-aged (>50 years)
al., 2016; Y. J. Lee et al., 2016; Osborn, Paasche-Orlow, et al., adults with chronic disease (Como, 2018; Y. J. Lee et al., 2016;
2011; Sun et al., 2013), two reported a significant, mediating Osborn, Paasche-Orlow, et al., 2011). Of the three studies,
effect (Brega et al., 2012; E. H. Lee et al., 2016). Both stud- one study (Y. J. Lee et al., 2016), which used participants’ self-
ies controlled for known demographic covariates such as age, reports of glycemic control, showed an acceptable framework
gender, education, marital status, treatment regimen (insulin fit, and an excellent framework fit was reported for the study
or oral hypoglycemic use), hemoglobin A1c level, as well as (Osborn, Paasche-Orlow, et al., 2011) that used patients’
duration of disease in the mediation analysis (Brega et al., medical records. One study validated the Nutbeam HL model
2012; E. H. Lee et al., 2016). (Nutbeam, 2008) in the context of obesity prevention using a
Other proposed mediators included patient-provider national sample of school-age children (N = 2,000; age range,
interaction (Guo et al., 2014; Hickman et al., 2016), de- 9-14 years); fit indices indicated a good fit (Intarakamhang &
cisional balance (Como, 2018), medication compliance Intarakamhang, 2017). One study conducted in China with
(Cho et al., 2008; Soones et al., 2017), preventive care use a sample of city-dwelling adults (N = 3,222) validated an
(Cho et al., 2008; Guo et al., 2014), information overload adapted framework of various HL theoretical models (Baker
(Como, 2018) and attitude and beliefs toward information [2006], Paasche-Orlow and Wolf [2007], and McCormack
(Crook et al., 2016). Only one study across a sample of [2009] models) and reported a good fit of the proposed
predominately White (66%), urban-dwelling adults (mean framework (Sun et al., 2013). The authors of the study did not
age, 53 years) found that patient-dentist communication clearly describe how study variables were operationalized,
and the frequent use of dental care services mediates the however (Sun et al., 2013). Two studies conducted in the U.S.
relationship between HL (navigation) and self-rated oral (Como, 2018; Jin et al., 2019) also adapted multiple theoreti-
health (p = .01) (Guo et al., 2014). The remaining stud- cal models (i.e. Paasche-Orlow and Wolf model [2007], Ban-
ies found no statistically significant mediation pathways dura’s self-efficacy theory [Bandura, 1977], health literacy
linking HL to health behaviors and outcomes (Cho et al., skills framework [Squires, Peinado, Berkman, Boudewyns,
2008; Crook et al., 2016; Hickman et al., 2016; Soones et & McCormack, 2012] and cognitive mediation model [Eve-
al., 2017). Only 3 of the 20 studies included in this review land & Dunwoody, 2001]) but failed to report fit indices.
assessed the interaction of HL and study outcomes (gly- Additionally, five studies (Chen, 2014; Crook et al., 2016;
cemic control, medication adherence), but the authors Hickman et al., 2016; Photharos et al., 2018; Zou et al., 2017)
did not describe this relationship as moderating (Osborn, that reported good to excellent fit indices were informed by
Paasche-Orlow et al., 2011; Schillinger et al., 2006; Soones theories that do not specifically address HL but are common-
et al., 2017). ly used in nursing and public health research to study health
behaviors and overall health outcomes: Orem’s theory of self- been tested in a single population; therefore, the validity and
care and Bandura’s social cognitive theory, theory of diffusion reliability of those measures could not be established (Intara-
of innovations, model of client health behavior, individual kamhang & Intarakamhang, 2017; E. H. Lee et al., 2016; Y. J.
and family self-management theory, and capability opportu- Lee et al., 2016; Sun et al., 2013; Zou et al., 2017). Also impor-
nity motivation and behavior model. (Bandura, 1977; Cox, tant is that the studies were predominantly across a conve-
1982; Michie, Stralen, van Stralen, & West, 2011; Orem, 2003; nience sample of female, urban-dwelling adults with less than
Rogers, 2002; Ryan & Sawin, 2009.) a high school education who were recruited from health care
facilities. Therefore, findings cannot be generalized to other
DISCUSSION populations that do not use the health care system due to lan-
To our knowledge, this is the first systematic review to guage barriers or a lack of health insurance. Finally, theory
critically appraise studies that have empirically tested the po- provides a systematic foundation and a logical pathway for il-
tential pathways linking HL to health behaviors and health lustrating the relationship among various study concepts and
outcomes. We found evidence to support that theoretically variables. However, only a limited number of studies (n = 12)
selected mediators (i.e., self-efficacy, disease knowledge, self- included in the review explained how theory informed the
care activities, and patient-provider communication) medi- selection and operationalization of study variables, delimit-
ate the identified relationship between HL and chronic dis- ing the generalizability of findings.
ease management, with self-efficacy as the commonly tested Findings from this review call for the need to use theo-
mediator (E. H. Lee et al., 2016; Y. J. Lee et al., 2016). Our retically grounded, methodologically rigorous research with
findings show that unless people possess adequate HL, they statistically powered sample sizes to adequately examine the
may perceive low confidence in their abilities to manage their interplay between HL and health behaviors or outcomes in
chronic diseases. In addition, improving people’s HL is an diverse study populations. For example, the studies included
essential first step to increasing their knowledge about their in this review exclusively used a cross-sectional design to test
disease, improving their ability to adequately perform self- the indirect pathways linking HL to health behaviors. Hence,
care activities, and effectively communicate and collaborate there is still a need for establishing temporality and causal-
with health care providers in their chronic disease man- ity using more rigorous study designs such as longitudinal
agement (Charlot et al., 2017; Chisholm-Burns, Spivey, & cohort design. Several studies have used longitudinal data to
Pickett, 2018). We also found evidence to support that inter- examine the role of HL on health behaviors and outcomes;
vention outcomes (glycemic control, medication adherence) however, they did not meet the inclusion criteria for this re-
differ by the HL levels of study participants, suggesting HL view because the authors did not specify a HL conceptual
as a moderator (Schillinger et al., 2006; Soones et al., 2017). framework to be tested (Kobayashi, Wardle, & Wagner, 2015;
This finding highlights an important implication for future Washington, Curtis, Waite, Wolf, & Paasche-Orlow, 2018). In
research, particularly in relation to intervention research as it addition, although a recent systematic review showed that HL
relates to the role of HL beyond mediation. has gained importance on the European health agenda, none
We identified several factors that may have contributed to of the studies identified from our extensive search of various
the mixed findings we reported: study design, selection bias, database were conducted in Europe (Sørensen et al., 2015).
small sample sizes, measurement errors, and non–theory- Further, among U.S.-based studies, all were conducted on fe-
guided operationalization of study variables. Although all male, English-speaking adults (Brega et al., 2012; Chen, 2014;
studies in this review aimed to examine the pathways link- Cho et al., 2008; Como, 2018; Crook et al., 2016; Guo et al.,
ing HL to health behaviors and outcomes, these studies ex- 2014; Hickman et al., 2016; Jin et al., 2019; Osborn, Cavana-
clusively used cross-sectional and a mixed-methods designs, ugh, et al., 2011; Osborn et al., 2010; Osborn, Paasche-Orlow,
which preclude causality and temporality. Secondly, only 7 et al., 2011; Schillinger et al., 2006; Soones et al., 2017). Al-
of 20 studies conducted sample size calculations and power though people who belong to ethnic/racial minority groups
analyses a priori (Chen, 2015; Como, 2018; Hou et al., 2018; and those with low English proficiency, particularly immi-
Intarakamhang & Intarakamhang, 2017; E. H. Lee et al., grants, are known to be disproportionately burdened by low
2016; Y. J. Lee et al., 2016; Photharos et al., 2018). The lack HL, they were excluded from the U.S.-based studies (Alper,
of statistical power in most of the studies could account for 2018; Wang et al., 2013). In particular, African immigrants,
the mixed findings reported. Thirdly, although all U.S.-based an exponentially increasing immigrant group in the U.S. with
studies used well-validated HL measures, the remaining worse health outcomes in comparison to other immigrant
studies either lacked psychometric testing results or had only groups, were excluded in all the U.S.-based studies (Anderson,
2015). Although there is a possibility that African immigrants HL conceptual frameworks, and the systematic selection and
were categorized as Black Americans in some of these studies, evaluation of variables that inform HL-focused studies. We
it has been established that people of African descent (Black, found evidence to support that HL is related to health be-
African immigrant, and Afro-Caribbean) in the U.S. have dif- haviors, particularly chronic disease management, through
ferent cultural and linguistic characteristics that affect their mediators such as self-efficacy and disease knowledge.
health outcomes differently. Therefore, there is a need to dis-
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acy Project European. (2012). Health literacy and public health: A

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Systematic Review

Empirically Tested Health Literacy Frameworks

Quality Assessments of Studies

Standard Strategies for

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Note. 1 = clearly discussed; 0 = not discussed.

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Study Characteristics and Main Findings

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Theoretical Frameworks of Health Literacy

How Framework Proposed Health Behaviors/ Fit Indices for Final

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Theoretical Frameworks of Health Literacy

Higher HL associated with lower Good model fit

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Theoretical Frameworks of Health Literacy

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Theoretical Frameworks of Health Literacy

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Theoretical Frameworks of Health Literacy

Family functioning influences self- problem solving,

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

Theoretical Frameworks of Health Literacy

HLRP: Health Literacy Research and Practice • Vol. 4, No. 1, 2020

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