| Literature DB >> 24282634 |
Jared P Schprechman1, Emily C Gathright, Carly M Goldstein, Kate A Guerini, Mary A Dolansky, Joseph Redle, Joel W Hughes.
Abstract
Background. The internet offers a potential for improving patient knowledge, and e-mail may be used in patient communication with providers. However, barriers to internet and e-mail use, such as low health literacy and cognitive impairment, may prevent patients from using technological resources. Purpose. We investigated whether health literacy, heart failure knowledge, and cognitive function were related to internet and e-mail use in older adults with heart failure (HF). Methods. Older adults (N = 119) with heart failure (69.84 ± 9.09 years) completed measures of health literacy, heart failure knowledge, cognitive functioning, and internet use in a cross-sectional study. Results. Internet and e-mail use were reported in 78.2% and 71.4% of this sample of patients with HF, respectively. Controlling for age and education, logistic regression analyses indicated that higher health literacy predicted e-mail (P < .05) but not internet use. Global cognitive function predicted e-mail (P < .05) but not internet use. Only 45% used the Internet to obtain information on HF and internet use was not associated with greater HF knowledge. Conclusions. The majority of HF patients use the internet and e-mail, but poor health literacy and cognitive impairment may prevent some patients from accessing these resources. Future studies that examine specific internet and email interventions to increase HF knowledge are needed.Entities:
Year: 2013 PMID: 24282634 PMCID: PMC3824560 DOI: 10.1155/2013/507910
Source DB: PubMed Journal: Nurs Res Pract ISSN: 2090-1429
Demographic characteristics (n = 124).
|
| Mean (SD) | |
|---|---|---|
| Age (years) | 69.85 (9.20) | |
| Sex | ||
| Male | 84 (70.6) | |
| Female | 35 (29.4) | |
| Race | ||
| Caucasian | 101 (84.9) | |
| Non-Caucasian | 18 (15.1) | |
| Education level | ||
| 8th grade or less | 1 (0.8) | |
| 9th to 11th | 8 (6.7) | |
| High school | 30 (25.2) | |
| Technical or trade | 17 (14.3) | |
| Some college | 29 (24.4) | |
| Bachelor's degree | 20 (16.8) | |
| Master's degree | 14 (11.8) | |
| Employment status | ||
| Retired | 79 (66.4) | |
| Retired but work part time | 22 (18.5) | |
| Retired but work full time | 2 (1.7) | |
| Work part time | 5 (4.2) | |
| Work full time | 10 (8.4) | |
| Homemaker | 1 (0.8) | |
| Marital status | ||
| Never married | 7 (5.9) | |
| Married | 79 (66.4) | |
| Widowed | 12 (10.1) | |
| Separated | 2 (1.7) | |
| Divorced | 19 (16.0) | |
| Current living arrangements | ||
| Live alone | 21 (17.6) | |
| Live w/a spouse | 78 (65.5) | |
| Live w/a friend permanently | 3 (2.5) | |
| Live w/a family member temporarily | 4 (3.4) | |
| Live w/a family member permanently | 13 (10.9) |
How connected are heart failure patients?
|
| |
|---|---|
| Has a doctor ever asked if you have internet access? | 28 (23.5) |
| Have you ever communicated with a doctor or another provider through e-mail? | 16 (13.4) |
| Has a doctor ever recommended a particular health/medical website? | 11 (9.2) |
| Have you ever looked up information about heart failure on the internet? | 54 (45.4) |
METER scores.
|
| |
|---|---|
| Low health literacy (0–20) | 5 (4.2) |
| Marginal health literacy (21–34) | 17 (14.3) |
| Functional health literacy (35–40) | 97 (81.5) |
Logistic regression analysis predicting internet use from age, education, and health literacy (METER).
|
| SE |
| 95% C.I. for | |
|---|---|---|---|---|
| Age | −.03 | .03 | .97 | .92–1.02 |
| Education | .38* | .16 | 1.46 | 1.06–2.01 |
| METER | .07 | .04 | 1.08 | .99–1.16 |
| Constant | −1.07 | |||
|
| ||||
|
| 10.92* | |||
| df | 3 | |||
*P < .05.
Logistic regression analysis predicting e-mail use from age, education, and health literacy (METER).
|
| SE |
| 95% C.I. for | |
|---|---|---|---|---|
| Age | −.03 | .02 | .97 | .92–1.01 |
| Education | .45** | .16 | 1.56 | 1.16–2.12 |
| METER | .08* | .04 | 1.09 | 1.01–1.17 |
| Constant | −2.10 | |||
|
| ||||
|
| 16.27** | |||
| df | 3 | |||
*P < .05, **P < .01.
Logistic regression analysis predicting e-mail use from age, education, and cognitive function (3MS).
|
| SE |
| 95% C.I. for | |
|---|---|---|---|---|
| Age | −.02 | .03 | .98 | .94–1.03 |
| Education | .35* | .16 | 1.42 | 1.04–1.95 |
| 3MS | .09* | .04 | 1.09 | 1.00–1.19 |
| Constant | −7.57 | |||
|
| ||||
|
| 15.20*** | |||
| df | 3 | |||
*P < .05, ***P < .001.
Note: 3MS Modified Minimental State.