Table 2.
Reference | Sample and setting | Health literacy measurement | Methodology | Key findings: Health literacy | Associated factors |
---|---|---|---|---|---|
Wolf, Feinglass, Thompson, and Baker (2010) |
n = 2,956; 65+ years Medicare enrollees English speaking Multisite: Cleveland, Houston, Tampa and Fort Lauderdale/Miami |
S-TOFHLA (divided into 7 categories instead of typical 3) | Cross-sectional questionnaire Interviewer-administered survey (1 hr in-home) Compared HL to self-rated physical function, mental health, and mortality rates |
Low HL associated with older age, non-White, lower income, less education, abstinence from alcohol consumption, less frequent PA and underweight Low HLs reported more activity limitations and worse baseline physical functioning and mental health Literacy could be causally related to physical functioning; decrease cognitive skills may lead to progressively lower understanding of how to stay healthy, when to seek medical attention, and how to properly follow medical regimens to recover from acute and care for chronic health conditions |
Graded relationship between literacy scores and baseline physical functioning (lowest 3 HL scores poorer physical function compared with highest HL category) Relationship to mental health was a threshold rather than continuous (Categories 1 and 2 worse mental health compared with Category 7) All-cause mortality rate greater for those with lowest HL |
Gerber, Cho, Arozullah, and Lee (2010) |
n = 450; 65+ years African American M = 78.2 years Caucasian M = 76.8 years Medicare recipients Chicago, IL |
S-TOFHLA | Cross-sectional questionnaire, interviewer-administered survey (in-home or medical center) Compared medication adherence by African American and Caucasian older adults |
More African Americans reported a history of hypertension (70.7% vs. 57.1%; p = .003) or diabetes (26.3% vs. 14.7%; p = .003), worse health status, lower health literacy, worse depression scores, and less social support compared with Caucasians (all, p < .001). African Americans were more likely to report running out of medications before refilling them and not always following physician instructions on how to take medications. No difference between races was observed in forgetting to take medications. |
Even after adjusting for differences in demographics, health literacy, depression, and social support, older African Americans reported following medication directions less often than older Caucasians. |
Ganzer, Insel, and Ritter (2012) |
n = 56; 65+ years M = 80.4 years English speaking Community dwelling NYC metro area |
S-TOFHLA | Cross-sectional survey Descriptive pilot study Relationship between working memory, HL, and recall of five signs of stroke |
Over 50% of the sample had high health literacy Signs of stroke recalled M = 2.9 ± 1.33 Stroke sign recall was associated with working memory (r = .38, p < .01), health literacy (r = .44, p < .01), education (r = .36, p < .01), and dementia (r = .54, p < .01). Working memory was positively associated with education (r =.58, p < .01), income that meets expenses (r = −.34, p < .05), health literacy (r = .57, p < .01), and age (r = −.33, p < .05). Health literacy was significantly related to education (r = .46, p < .01). Using regression, health literacy was the best predictor of stroke recall (β = .56, p < .01). |
With additional regression analysis considering working memory, health literacy and dementia together, both health literacy (β = .28, p < .05) and dementia (β = .44, p < .01) were significantly associated with recall of signs of stroke. Results demonstrate that working memory and health literacy were significantly associated with recall of the signs of stroke. Only health literacy remained a significant predictor of the recall of the warning signs of stroke When MMSE (dementia) was included in regression, health literacy remained a significant predictor of recall, but dementia was more strongly associated with recall. |
McDougall, Mackert, and Becker (2012) |
n = 45; 65+ years M = 77.11 years Community-residing older adults Central Texas metro area |
REALM | Pilot study Cross-sectional Relationship between health literacy, memory performance, and performance-based functional ability |
Health literacy not related to education (.19) or age (−.15) Education and cognition (.30) were associated significantly. Health literacy was associated with RBMT memory performance groups (normal vs. poor; .25) and DAFS-E scores (.50). |
Education was not related to health literacy |
Patel (2010); Detroit, MI |
n = 62; 65+ years M = 73.2 years African American Primary care setting |
S-TOFHLA, NVS | Evaluated utility of NVS and S-TOFHLA | No significant differences in NVS and S-TOFHLA scores between men and women. Fifty percent of participants were deemed sufficiently literate using S-TOFHLA in comparison with 42% using NVS. Mean time to complete NVS was 11.7 min. Previous research in younger population reports completion time to be 2.9 min. |
Patient’s educational level and age were better predictors than NVS score for assessing health literacy in this population. |
Mosher, Lund, Kripalani, and Kaboli (2012) |
n = 310, 65+ years M = 74 years Veterans from a primary care clinic English speaking Iowa City, VA Center |
REALM | Cross-sectional Face-to-face interview Examined association of health literacy with medication knowledge, adherence, and adverse drug events |
Lower health literacy was associated with less knowledge of medication names and purposes. Patients with low health literacy knew 32.2% of medications by name, as compared with 54.6% of medication names for patients with marginal health literacy, and 60.8% for patients with adequate health literacy (p < .001). Lower literacy group knew the purpose of 61.8% of their medications, compared with 77.4% and 81.4% in the marginal and adequate literacy groups, respectively (p < .001). |
Health literacy was not associated with self-reported medication adherence or adverse drug events Health literacy was not associated with number of prescribed medications |
Cordasco, 2011 |
n = 160, age 65+ years M = 72.0 years Spanish speaking Diagnosed with diabetes for at least 1 year Clinics associated with a large safety-net hospital LA County, CA |
Spanish REALM, S-TOFHLA, and SILS | Cross-sectional One-on-one interview Evaluated accuracy of SILS in detecting IHL in monolingual Spanish speakers; investigates best predictor SILS or education level |
S-TOFHLA indicated 84% had IHL Best performing SILS question, “How confident are you filling out medical forms by yourself?” AUROC curve of 0.82; high sensitivity (fewer than 1 out of 10 with IHL will be missed), low specificity (7 out of 10 with IHL will be misclassified) Remaining two SILS questions had AUROC curves less than 0.50. Educational achievement AUROC curve was 0.88; education cutoff of 6 years or less had a specificity to 0.81 and sensitivity of 0.83 |
Use single items as screen for IHL in older U.S. monolingual Spanish speakers Should either use the “confidence with forms” SILS, being aware of its specificity limitations, or a single question assessing educational achievement |
Bickmore et al. (2010) |
n = 33; 65+ years English speaking Boston Medical Center |
TOFHLA | Two-armed intervention trial; evaluated the use of computer animated characters as vehicles for health education and behavioral change counseling | Participants with inadequate health literacy had lower levels of computer literacy compared with participants with adequate health literacy, although this difference was only trending toward significance, likely due to the smaller sample size | Overall, there were very few differences in measures of acceptance and usability between patients with adequate and inadequate health literacy, suggesting that ECAs are approachable and usable by patients regardless of health literacy level. In the few measures in which there were significant or near-significant differences on health literacy, these were mostly in favor of patients with inadequate health literacy. |
Note. S-TOFHLA = Short Test of Functional Health Literacy in Adults; HL = health literacy; MMSE = minimum mean square error; REALM = Rapid Estimate of Adult Literacy in Medicine; NVS = Newest Vital Sign; SILS = Single Item Literacy Screening; ECA = Embodied Conversational Agents; PA = physical activity; RBMT = rivermead behavioral memory test; DAFS-E = direct assessment of functional status-extended; IHL = inadequate health literacy;
AUROC = areas under the receiving-operator characteristic.