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eHealth resources and interventions promise to promote favorable behavior change, self-efficacy, and knowledge acquisition, thereby improving health literacy. However, individuals with limited eHealth literacy may find it difficult to identify, understand, and benefit from eHealth use. It is necessary to identify the self-assessed eHealth literacy of those who use eHealth resources to classify their eHealth literacy levels and to determine the demographic characteristics associated with higher and lower eHealth literacy skills.
This study aimed to identify notable factors closely associated with limited eHealth literacy among Chinese male populations to provide some implications for clinical practice, health education, medical research, and public health policy making.
We hypothesized that participants’ eHealth literacy status was associated with various demographic characteristics. Therefore, we elicited the following information in the questionnaire: age and education, self-assessed disease knowledge, 3 well-developed health literacy assessment tools (ie, the All Aspects of Health Literacy Scale, eHealth Literacy Scale, and General Health Numeracy Test), and the 6
All data from the 543 returned questionnaires were valid according to the validation criteria. By interpreting these descriptive statistics, we found that 4 factors were significantly correlated with participants’ limited eHealth literacy: older age, lower education attainment, lower levels of all aspects of health literacy (functional, communicative, and critical), and weaker beliefs and self-confidence in internal drivers and strengths to stay healthy.
By applying logistic regression modeling, we ascertained 4 factors that were significantly correlated with limited eHealth literacy among Chinese male populations. These relevant factors identified can inform stakeholders engaging in clinical practice, health education, medical research, and health policy making.
Literacy related to health information is becoming a critical factor relevant to health status [
The effective delivery and uptake of eHealth interventions calls for eHealth knowledge and skills necessary to navigate health-related websites, platforms, and systems, that is, eHealth literacy. eHealth literacy has been defined as “the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem” by Norman and Skinner [
Personal characteristics and experiences unique to each individual can impact their subsequent health-promoting behaviors [
There are mixed findings regarding the association between sex and health literacy. Some studies have ascertained an association between male sex and limited health literacy [
This study aimed to identify notable factors closely associated with limited eHealth literacy among Chinese male populations to provide some implications for clinical practice, health education, medical research, and public health policy making.
The questionnaire included the following information: (1) age and education, (2) self-assessed disease knowledge, (3) 3 well-developed health literacy assessment tools (ie, the All Aspects of Health Literacy Scale [AAHLS] [
Previous studies have found a strong association between poor eHealth literacy and lower levels of functional, communicative, and CRHL [
The “Internal” locus of control has been found to be associated with health-promoting behaviors, health risk-reducing actions, and knowledge about health problems [
Using randomized sampling, we recruited survey participants from the Qilu Hospital of Shandong University, China. Those included in this study must (1) be aged ≥18 years, (2) have 6 years of or over of schooling experience to understand the questionnaire item, and (3) voluntarily participate in the survey. We conducted face-to-face contact with Chinese patients attending the outpatient clinic of Qilu Hospital and those who were hospitalized in this hospital to identify those who satisfied the inclusion criteria, informed them about the purpose of the survey, and asked them to participate in the web-based survey as scheduled. We conducted a power analysis to determine a sample size of 218 participants. A total of 589 eligible patients were included.
The questionnaire was administered via
On August 26, 2022, responses to the questionnaire were downloaded from
This study was approved by the Ethics Review Board of the Qilu Hospital of Shandong University, China (KYLL-202208-026). The study data were anonymized to protect the privacy and confidentiality of the study participants. As the participants voluntarily participated in the survey to support and promote academic research, no compensation was provided for them as per the common practice in China.
The distribution of eHealth literacy sum scores among male participants shows that most participants scored between 16 and 26. This score range means that most participant returned a “disagree” or “unsure” response to the 8 questions items in the eHEALS. In other words, they did not think that they could effectively use eHealth resources or they were unsure about their eHealth literacy skills.
Drawing on Turkey hinges, we identified 3 thresholds of eHealth literacy among Chinese male participants (valid number: 543): inadequate=13-22 (representing 50% of the sum score for the Chinese version of the eHEALS [CH-eHEALS]), problematic=23-24 (representing 25% of the sum score for CH-eHEALS), and sufficient=25-40 (representing 25% of the total score for CH-eHEALS), as shown in
Thresholds of eHealth literacy scale (eHEALS): inadequate (22 and below), problematic (23-24), and sufficient (25 or above).
|
Percentiles | ||||||
|
5 | 10 | 25 | 50 | 75 | 90 | 95 |
Weighted average, eHEALS_SUMa | 16.00 | 17.00 | 19.00 | 22.00 | 24.00 | 28.00 | 31.00 |
Tukey hinges, eHEALS_SUM | N/Ab | N/A | 19.00 | 22.00 | 24.00 | N/A | N/A |
aeHEALS_SUM: the sum scores of the eHEALS.
bN/A: not applicable.
The collinearity statistics, including the variance inflation factor and tolerance in
Collinearity statistics.
Predictor variables | Tolerance | VIFa |
Age | 0.93 | 1.08 |
Education | 0.85 | 1.17 |
Disease knowledge | 0.99 | 1.01 |
FHL_SUMb | 0.94 | 1.06 |
COHL_SUMc | 0.87 | 1.16 |
MHLC_SUMd | 0.86 | 1.17 |
GHNT_SUMe of correct responses | 0.99 | 1.01 |
CRHL_SUMf | 0.97 | 1.03 |
aVIF: variance inflation factor.
bFHL_SUM: sum of the functional health literacy subscale of the All Aspects of Health Literacy Scale.
cCOHL_SUM: sum of the communicative health literacy subscale of the All Aspects of Health Literacy Scale.
dMHLC_SUM: sum of the Multidimensional Health Locus of Control scales.
eGHNT_SUM: sum of the General Health Numeracy Test responses.
fCRHL_SUM: sum of the critical health literacy subscale of the All Aspects of Health Literacy Scale.
Limited eHealth literacy was also strongly associated with a range of health knowledge, skills, behaviors, and beliefs as measured by the FHL and COHL subscales of the AAHLS and the MHLC. The second item of the FHL subscale was, “When you need help, can you easily get hold of someone to assist you?” The responses were coded as 1=not applicable, 2=rarely, 3=sometimes, and 4=often, and “often” was used as the reference category. It was found that when a Chinese male participant was “rarely” able to easily secure help from others when needing help, the odds of being in the limited eHealth literacy category increased by 232% (FHL2, rarely: OR 3.32, 95% CI 1.62-6.82;
The second item (COHL2) of the COHL was “When you talk to a doctor or nurse, do you ask the questions you need to ask?” We coded the responses in the same way as for the FHL subscale. It was found that compared with male participants who “rarely” asked the questions that they needed to ask health professionals, among those who responded “often” and “sometimes,” their odds of being in the limited eHealth literacy group decreased significantly by 64% (COHL2, often: OR 0.36, 95% CI 0.19-0.69;
Finally, we used the MHLC (Form A) to measure health beliefs among male participants. Responses were coded as 1=strongly disagree, 2=moderately disagree, 3=slightly disagree, 4=slightly agree, 5=moderately agree, and 6=strongly agree. Thus, an increase in the sum scores of MHLC questions indicated strong internal drivers in managing one’s own health. The results showed that with an increase of one unit in the sum score of the MHLC scales, the odds of a Chinese male participant being in the limited eHealth literacy group decreased by 6% at a statistically significant level (sum of the MHLC scales: OR 0.94, 95% CI 0.90-0.98;
Factors associate with limited eHealth literacy among Chinese male populations. Predicted membership is limited eHealth literary.
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Age | 0.02 (0.01) | 5.01 (1) | .03 | 1.02 (1.00-1.05) |
Education (reference: postgraduate) | N/Aa | 8.96 (5) | .11 | N/A |
Education (year 6) | 3.21 (1.13) | 8.09 (1) | <.001 | 24.69 (2.71-224.85) |
Education (year 9) | 2.82 (1.11) | 6.45 (1) | .01 | 16.75 (1.90-147.38) |
Education (year 12) | 2.83 (1.11) | 6.52 (1) | .01 | 17.01 (1.93-149.92) |
Education (diploma) | 2.75 (1.11) | 6.19 (1) | .01 | 15.72 (1.79-137.68) |
Education (university) | 3.03 (1.12) | 7.35 (1) | .01 | 20.79 (2.32-186.53) |
FHL2b (reference: often) | N/A | 12.32 (3) | .01 | N/A |
FHL2 (not applicable) | 0.64 (0.37) | 3.04 (1) | .08 | 1.89 (0.92-3.87) |
FHL2 (rarely) | 1.20 (0.37) | 10.69 (1) | <.001 | 3.32 (1.62-6.826.82) |
FHL2 (sometimes) | 0.19 (0.26) | 0.52 (1) | .47 | 1.21 (0.73-2.01) |
COHL2c (reference: rarely) | N/A | 9.63 (2) | .01 | N/A |
COHL2 (often) | 1.02 (0.33) | 9.46 (1) | <.001 | 0.36 (0.19-0.69) |
COHL2 (sometimes) | 0.80 (0.32) | 6.26 (1) | .01 | 0.45 (0.24-0.84) |
MHLC_SUMd | 0.06 (0.02) | 8.83 (1) | <.001 | 0.94 (0.90-0.98) |
Constant | 1.17 (1.30) | 0.81 (1) | .37 | 0.31 (N/A) |
aN/A: not applicable.
bFHL2: item 2 of the functional health literacy subscale.
cCOHL2: item 2 of the communicative health literacy subscale.
dMHLC_SUM: sum of the Multidimensional Health Locus of Control scales.
Next, we analyzed factors associated with responses to individual items of eHealth literacy. We labeled the following responses as belonging to the limited eHealth literacy category: “strongly disagree,” “disagree,” and “unsure,” in comparison with the responses of “agree” and “strongly agree.” Informed by Manganello et al [
We also found that limited eHealth literacy, specifically not knowing “what health resources were available on the internet” (eHEALS1), was strongly associated with an increased tendency and frequency to challenge health and medical professionals based on one’s own research among Chinese male participants. For example, we coded the responses to the fourth item of critical health literacy “Are you the sort of person who might question your doctor or nurse’s advice based on your own research?” as 1=yes, definitely, 2=maybe/sometimes, and 3=not really. The regression modeling used “3=not really” as the reference category and revealed that when a male participant responded “definitely” or “maybe/sometimes,” his odds of being in the limited eHealth literacy group increased significantly by 104% (CRHL4, yes: OR 2.04, 95% CI 1.20-3.46;
The second item (eHEALS2) of the eHEALS was “I know where to find helpful health information on the internet.” We found that education continued to be an important predictor of limited eHealth literacy in terms of one’s ability to “find helpful health information on the internet.” Using “postgraduate or above” as the reference educational level, it was found that lower educational levels predicted larger increases in the odds of male participants having trouble identifying helpful health information on the web. The largest increase in the odds of experiencing difficulties in identifying useful health information was found among Chinese male participants with year 6 or below education (OR 10.44, 95% CI 2.78-39.19;
Factors and health behaviors associated with limited eHealth literacy responses (item 1 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
COHL_SUMa | 0.22 (0.07) | 9.77 (1) | <.001 | 1.25 (1.09-1.43) |
CRHL4b (reference: not really) | N/Ac | 10.41 (2) | .01 | N/A |
CRHL4 (yes) | 0.71 (0.27) | 7.00 (1) | .01 | 2.04 (1.20-3.46) |
CRHL4 (sometimes) | 0.71 (0.24) | 8.83 (1) | <.001 | 2.04 (1.28-3.27) |
Constant | −0.70 (0.45) | 2.36 (1) | .13 | 0.50 (N/A) |
aCOHL_SUM: sum of the communicative health literacy subscale.
bCRHL4: item 4 of the critical health literacy subscale.
cN/A: not applicable.
Factors and health behaviors associated with limited eHealth literacy responses (item 2 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Education (reference: postgraduate) | N/Aa | 12.42 (5) | .03 | N/A |
Education (year 6) | 2.35 (0.67) | 12.09 (1) | <.001 | 10.44 (2.78-39.19) |
Education (year 9) | 1.75 (0.64) | 7.38 (1) | .01 | 5.75 (1.63-20.35) |
Education (year 12) | 1.83 (0.65) | 7.97 (1) | <.001 | 6.23 (1.75-22.16) |
Education (diploma) | 1.85 (0.65) | 8.18 (1) | <.001 | 6.39 (1.79-22.77) |
Education (university) | 1.88 (0.68) | 7.67 (1) | .01 | 6.53 (1.73-24.67) |
COHL3b (reference: rarely) | N/A | 6.83 (2) | .03 | N/A |
COHL3 (often) | −0.65 (0.31) | 4.42 (1) | .04 | 0.52 (0.29-0.96) |
COHL3 (sometimes) | −0.75 (0.29) | 6.62 (1) | .01 | 0.47 (0.27-0.84) |
CRHL1c (reference: rarely) | N/A | 8.57 (2) | .01 | N/A |
CRHL1 (often) | −0.84 (0.29) | 8.49 (1) | <.001 | 0.43 (0.24-0.76) |
CRHL1 (sometimes) | −0.45 (0.27) | 2.72 (1) | .10 | 0.64 (0.37-1.09) |
Constant | 0.34 (0.69 | 0.25 (1) | .62 | 1.41 (N/A) |
aN/A: not applicable.
bCOHL3: item 3 of the communicative health literacy subscale.
cCRHL1: item 1 of the critical health literacy subscale.
We found that when a male participant reported “often” or “sometimes” for “making sure that they explain anything that you do not understand, when talking to a doctor or nurse,” compared with those who “rarely” did so, their odds of having trouble identifying helpful health information on the internet reduced significantly by 48% and 53%, respectively (COHL3, often: OR 0.52, 95% CI 0.29-0.96;
Finally, it was found that greater interest in “finding out lots of different information about your health” (CRHL1) was associated with reduced odds of having trouble identifying helpful health information on the internet. Specifically, when a male participant reported “often” or “sometimes” for searching for diverse information about one’s own health, his odds of reporting difficulties to find useful information on the internet reduced by 57% and 36%, respectively (CRHL1, often: OR 0.43, 95% CI 0.24-0.76;
The third item (eHEAL3) was “I know what health information is available on the internet.” In our study, we coded functional health literacy as 1=often, 2=sometimes, and 3=rarely. As the 3 questions of the FHL subscale were related to one’s independence in comprehending health information (FHL1), securing others’ help when in need (FHL2), and completing official documents (FHL3), the higher the sum scores of the FHL subscale, the greater one’s functional health literacy. Our study found that with an increase of one score in the sum of FHL subscale, the odds of a male participant “not knowing what health information is available on the internet” reduced by 15% (sum of the FHL subscale: OR 0.85, 95% CI 0.74-0.97;
The fourth item (eHEALS4) of the eHEALS was “I know how to find helpful health information the internet.” We found that education was a significant predictor of Chinese male participants’ capability to articulate strategies to find helpful web-based health information. Statistically significant increases in the odds of not having the knowledge to find helpful health information were found among Chinese male participants with year 6 education (OR 5.95, 95% CI 1.65-21.46;
It was interesting to find out that there were no statistically notable changes in the odds of not knowing how to find helpful web-based health information among male participants who reported either “often” or “rarely” challenging the advice from health and medical professionals based on their own research. On the contrary, when a male individual reported that he only “sometimes or maybe” challenged the advice from health professionals, his odds of not knowing how to find helpful web-based health information was reduced significantly by 51% (CRHL4, sometimes: OR 0.49, 95% CI 0.30-0.81;
Factors and health behaviors associated with limited eHealth literacy responses (item 3 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
FHL_SUMa | −0.17 (0.07) | 6.10 (1) | .01 | 0.85 (0.74-0.97) |
MHLCb_A13 (reference: strongly agree) | N/Ac | 12.32 (5) | .03 | N/A |
MHLC_A13 (strongly disagree) | 0.68 (0.42) | 2.61 (1) | .11 | 1.98 (0.86-4.54) |
MHLC_A13 (moderately disagree) | 0.16 (0.35) | 0.21 (1) | .65 | 1.18 (0.59-2.35) |
MHLC_A13 (slightly disagree) | 0.52 (0.37) | 2.00 (1) | .16 | 1.69 (0.82-3.50) |
MHLC_A13 (slightly agree) | 0.66 (0.41) | 2.64 (1) | .10 | 1.94 (0.87-4.33) |
MHLC_A13 (moderately agree) | −0.33 (0.38) | 0.75 (1) | .39 | 0.72 (0.35-1.51) |
Constant | 2.02 (0.56) | 13.01 (1) | <.001 | 7.52 (N/A) |
aFHL_SUM: sum of the functional health literacy subscale.
bMHLC: Multidimensional Health Locus of Control.
cN/A: not applicable.
Factors and health behaviors associated with limited eHealth literacy responses (item 4 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Education (reference: postgraduate) | N/Aa | 13.23 (5) | .02 | N/A |
Education (year 6) | 1.78 (0.65) | 7.41 (1) | .01 | 5.95 (1.65-21.46) |
Education (year 9) | 1.85 (0.64) | 8.38 (1) | <.001 | 6.35 (1.82-22.18) |
Education (year 12) | 1.26 (0.64) | 3.87 (1) | .05 | 3.51 (1.00-12.29) |
Education (diploma) | 1.30 (0.64) | 4.14 (1) | .04 | 3.66 (1.05-12.76) |
Education (university) | 1.50 (0.67) | 5.06 (1) | .02 | 4.48 (1.21-16.54) |
CRHL4b (reference: no) | N/A | 8.02 (2) | .02 | N/A |
CRHL4 (often) | −0.46 (0.27) | 2.81 (1) | .09 | 0.63 (0.37-1.08) |
CRHL4 (sometimes) | −0.71 (0.25) | 7.98 (1) | <.001 | 0.49 (0.30-0.81) |
Constant | −0.24 (0.64) | 0.14 (1) | .70 | 0.78 (N/A) |
aN/A: not applicable.
bCRHL4: item 4 of the critical health literacy subscale.
The fifth item (eHEALS5) of the eHEALS was “I know how to use the health information I find on the internet to help me.” When male participants have an education level lower than the reference category (postgraduate), their odds of not knowing “how to use the health information I find on the internet to help myself” increased significantly (year 6 education: OR 6.73, 95% CI 1.62-27.99;
Additionally, when male participants reported that they were “rarely” able to “easily get hold of someone to help me when I need help” (FHL2, rarely) and simply never thought of seeking others’ help (FHL2, not applicable), their odds of not knowing “how to use the health information I find on the internet to help myself” increased significantly by 146% and 139%, respectively (FHL2, rarely: OR 2.46, 95% CI 1.31-4.65;
Factors and health behaviors associated with limited eHealth literacy responses (item 5 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Education (reference: postgraduate) | N/Aa | 12.57 (5) | .03 | N/A |
Education (year 6) | 1.91 (0.73) | 6.88 (1) | .01 | 6.73 (1.62-27.99) |
Education (year 9) | 1.89 (0.71) | 7.04 (1) | .01 | 6.60 (1.64-26.59) |
Education (year 12) | 2.41 (0.72) | 11.16 (1) | <.001 | 11.16 (2.71-45.93) |
Education (diploma) | 2.14 (0.72) | 8.85 (1) | <.001 | 8.48 (2.07-34.70) |
Education (university) | 1.91 (0.73) | 6.78 (1) | .01 | 6.77 (1.60-28.54) |
FHL2b (reference: often) | N/A | 16.34 (3) | <.001 | N/A |
FHL2 (not applicable) | 0.87 (0.37) | 5.47 (1) | .02 | 2.39 (1.15-4.96) |
FHL2 (rarely) | 0.90 (0.32) | 7.74 (1) | .01 | 2.46 (1.31-4.65) |
FHL2 (sometimes) | −0.11 (0.25) | 0.19 (1) | .66 | 0.90 (0.56-1.45) |
Constant | −1.18 (0.72) | 2.68 (1) | .10 | 0.31 (N/A) |
aN/A: not applicable.
bFHL2: item 2 of the functional health literacy subscale.
The sixth item (eHEALS6) of the eHEALS was “I have the necessary skills to evaluate the health resources I find on the internet.” Again, the finding was similar to that presented in
The seventh item (eHEALS7) of the eHEALS was “I can distinguish between high- and low-quality health information on the internet.” The results were very similar to those related to eHEAL6 regarding health information appraisal skills: lower education levels predicted increased odds of self-reported lack of ability to ascertain the credibility and quality of web-based health information, with the largest increase in such odds identified among Chinese male participants with year 9 education (OR 10.10, 95% CI 2.53-40.24;
The last item (eHEALS8) was “I feel confident in using information from the internet to make health decisions.” We found that an increase in age predicted increased odds of self-reported lack of confidence in using web-based health information to make health decisions among male Chinese participants (age: OR 1.02, 95% CI 1.01-1.04;
Factors and health behaviors associated with limited eHealth literacy responses (item 6 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Education (reference: postgraduate) | N/Aa | 12.12 (5) | .03 | N/A |
Education (year 6) | 1.72 (0.67) | 6.53 (1) | .01 | 5.56 (1.49-20.75) |
Education (year 9) | 1.92 (0.66) | 8.51 (1) | <.001 | 6.79 (1.87-24.59) |
Education (year 12) | 1.67 (0.66) | 6.49 (1) | .01 | 5.32 (1.47-19.26) |
Education (diploma) | 1.41 (0.65) | 4.68 (1) | .03 | 4.09 (1.14-14.63) |
Education (university) | 1.14 (0.67) | 2.92 (1) | .09 | 3.13 (0.85-11.57) |
CRHL1b (reference: no) | N/A | 8.44 (2) | .01 | N/A |
CRHL1 (often) | −0.80 (0.28) | 8.36 (1) | <.001 | 0.45 (0.26-0.77) |
CRHL1 (sometimes) | −0.54 (0.26) | 4.23 (1) | .04 | 0.58 (0.35-0.98) |
MHLC_SUMc | −0.04 (0.02) | 4.66 (1) | .03 | 0.96 (0.92-1.00) |
Constant | 0.72 (0.79) | 0.82 (1) | .36 | 2.05 (N/A) |
aN/A: not applicable.
bCRHL1: item 1 of the critical health literacy subscale.
cMHLC_SUM: sum of the Multidimensional Health Locus of Control scales.
Factors and health behaviors associated with limited eHealth literacy responses (item 7 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Education (reference: postgraduate) | N/Aa | 13.56 (5) | .02 | N/A |
Education (year 6) | 1.87 (0.72) | 6.82 (1) | .01 | 6.48 (1.59-26.32) |
Education (year 9) | 2.31 (0.71) | 10.75 (1) | <.001 | 10.10 (2.53-40.24) |
Education (year 12) | 2.13 (0.71) | 9.13 (1) | <.001 | 8.43 (2.11-33.63) |
Education (diploma) | 2.05 (0.70) | 8.53 (1) | <.001 | 7.80 (1.97-30.96) |
Education (year 6) | 1.62 (0.72) | 5.09 (1) | .02 | 5.03 (1.24-20.49) |
MHLC_SUMb | −0.05 (0.02) | 5.71 (1) | .02 | 0.95 (0.92-0.99) |
Constant | −0.03 (0.82) | 0.00 (1) | .97 | 0.97 (N/A) |
aN/A: not applicable.
bMHLC_SUM: sum of the Multidimensional Health Locus of Control scales.
Factors and health behaviors associated with limited eHealth literacy responses (item 8 of the eHealth Literacy Scale).
|
B (SE) | Wald ( |
Exponential, B (95% CI) | |
Age | 0.02 (0.01) | 6.27 (1) | .01 | 1.02 (1.01-1.04) |
COHL2a (reference: no) | N/Ab | 13.30 (2) | <.001 | N/A |
COHL2 (often) | −0.88 (0.26) | 11.11 (1) | <.001 | 0.41 (0.25-0.70) |
COHL2 (sometimes) | −0.29 (0.26) | 1.23 (1) | .27 | 0.75 (0.45-1.25) |
Constant | 0.25 (0.48) | 0.28 (1) | .60 | 1.29 (N/A) |
aCOHL2: item 2 of the communicative health literacy subscale.
bN/A: not applicable.
By applying logistic regression modeling, we explored the factors associated with limited eHealth literacy among the Chinese male population. It has been found that 4 factors were significantly correlated with their limited eHealth literacy, as discussed in the following principal findings.
This finding is consistent with those reported in previous studies. Jensen et al [
Interestingly, as we found, the tendency and frequency of challenging suggestions from medical professionals based on one’s own research (CRHL) were common behaviors among Chinese male individuals lacking eHealth literacy, specifically not knowing “what health resources were available on the internet” (eHEALS1) and “how to find helpful health information the internet” (eHEALS4). This finding was reaffirmed by the 2 highly experienced Chinese clinicians in this study, with more than 20 years of work at Qilu Hospital, China.
This finding supports those of several previous studies. As shown by Neter and Brainin [
However, Milne et al [
Education was a notable predictor of Chinese male participants’ capability to articulate strategies to find helpful web-based health information, as found in our study. This finding confirms those of Knapp et a [
The results of our study showed that higher levels of beliefs in internal drivers and strengths to stay healthy predicted notable decreases in the odds of self-reported lack of essential web-based health information appraisal skills. This parallels the finding reported by Aponte and Nokes [
There is no study in the literature that has directly correlated people’s status of eHealth literacy with their “Internal” locus of control, that is, their beliefs in internal drivers and strengths to stay healthy. This study is the first to find such a direct correlation. We will conduct future studies to further ascertain this association in other Chinese populations with different demographic characteristics. Hopefully, as informed by this study, researchers will carry out similar studies to pinpoint the relationship between eHealth literacy status and the “Internal” locus of control to fill the gap in the literature.
This study has implications for clinical practice, health education, medical research, and public health policy-making. The 4 important predictors of limited eHealth literacy could serve as important indicators for screening individuals with limited eHealth literacy skills to deliver targeted education and interventions. Knowledge, skills, beliefs, and practices related to the 4 ascertained predictive factors can be integrated into public health education on eHealth resources and interventions to improve individuals’ eHealth literacy. Medical researchers may gain insights into the topic of limited eHealth literacy and its underlying factors. Informed by this study, they can verify the factors ascertained in this study and identify additional contributors in future research. Finally, our research results and findings may provide implications for public health policy making in the future.
This study analyzed factors and health behaviors associated with limited eHealth literacy among Chinese male participants. However, self-reported literacy skills do not always align with the actual ability to comprehend, use, and appraise web-based health information [
By applying logistic regression modeling, we found that limited eHealth literacy among Chinese male populations was closely associated with four factors: (1) older age, (2) lower educational attainment, (3) lower levels of all aspects of health literacy (functional, communicative, and critical), and (4) weaker beliefs and self-confidence in internal drivers and strengths to stay healthy. These predictive factors of limited male eHealth literacy can provide implications for clinical practice, health education, medical research, and health policy making.
Descriptive statistics of the study participants.
All Aspects of Health Literacy Scale
Chinese version of the eHealth Literacy Scale
communicative health literacy
critical health literacy
eHealth Literacy Scale
functional health literacy
General Health Numeracy Test
Multidimensional Health Locus of Control
odds ratio
Swedish version of the eHealth Literacy Scale
Data are available upon reasonable request to author YS (victorsyhz@hotmail.com).
None declared.