JMIR Publications

ABSTRACT

Background:

eHealth resources and interventions have promised to promote favorable behavior change, self-efficacy, and knowledge acquisition, improving health literacy. However, individuals with limited eHealth literacy may find it difficult to identify, understand, and benefit from their 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 what demographic characteristics may be associated higher and lower eHealth literacy skills.

Objective:

This study aimed to identify significant 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.

Methods:

We hypothesized that the participants’ eHealth literacy status was associated with various demographic characteristics. Therefore, we elicited the following information in the questionnaire: (1) age and education; (2) self-assessed disease knowledge; and (3) 3 well-developed health literacy assessment tools (ie, All Aspects of Health Literacy Scale (AAHLS), the eHealth Literacy Scale (eHEALS), the General Health Numeracy Test (GHNT-6), and the 6 ‘Internal’ items on health beliefs and self confidence in the Multidimensional Health Locus of Control (MHLC) Scales (MHLC). Using randomized sampling, we recruited survey participants from Qilu Hospital Affiliated to Shandong University, China. After validating the data collected through an online questionnaire survey via wenjuanxing, we coded all valid data according to the predefined coding schemes of Likert scales with different point (score) ranges. We then calculated the total scores of the subsections of the AAHLS, and the total scores of the 2 health literacy scales (eHEALS and GHNT-6). We finally utilize logistic regression modelling to associate the scores of the eHEALS with the scores of the AAHLS, the GHNT-6, and age and education, to ascertain factors significantly associated with limited eHealth literacy among Chinese male populations.

Results:

All data in the 543 returned questionnaires were valid according to the validation criterion. The informants were aged 45.73 (SD=10.31) on average. All of them were male. Their average educational attainment was just below Year 12 schooling (2.91, SD=1.35). They rated their disease knowledge between ‘knowing a lot’ and ‘knowing some’ (2.34, SD=0.98). They basically ‘sometimes’ needed help to read health-related information (6.83, SD=1.57), they ‘sometimes’ knew how to effectively communicate with doctors and nurses (5.73, SD=1.45), and they were ‘sometimes’ critical about health information (11.33,SD=2.009), respectively. The mean score for each question in the GHNT was determined at 1.55 (SD=0.50), 1.12 (SD=0.33), 1.14 (SD=0.35), 1.94 (SD=0.27), 1.88 (SD=0.32), and 1.84 (SD=0.37), respectively. And each participant returned an average of 2.52 (SD=1.00) correct responses to the 6 numeracy questions. These mean scores show that a large share of participants answered the 6 questions in the GHNT wrongly, especially Questions 1, 4, 5, and 6. The mean score for eHealth literacy was 22.01 (SD=4.50), showing that they were basically unsure about their ability to use eHealth resources and interventions. Most participants scored between 16 and 26 on the eHEALS scale, indicating that they did not think that they could effectively use eHealth resources or they were unsure about their eHealth literacy skills. Drawing on Turkey’s Hinges, we identified 3 thresholds of eHealth literacy among Chinese male participants: inadequate = 13 to 22, problematic = 23 to 24, and sufficient = 25 to 40, which represents 50%, 25%, and 25% of the sum score for the Chinese version of the eHEALS, respectively. We combined the 2 groups of inadequate and problematic eHealth literacy into 1 category of limited eHealth literacy, and then used logistic regression to explore factors associated with limited eHealth literacy among Chinese male populations. Through interpreting these descriptive statistics, we found that 4 factors were significantly correlated with the participants’ limited eHealth literacy: (1) age increase, (2) lower education 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.

Conclusions:

Applying logistic regression modelling, we ascertained 4 factors 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.