E-health literacy levels of high school students in Turkey: results of a cross-sectional study

Effective use of online COVID-19 information and eHealth information literacy among US university students

Objective: This study aimed to understand students' ability to search the Internet for COVID-19 information and apply it to evaluate the veracity of specific statements. Participants: Undergraduate students (N = 239) at a US university completed a Web-based questionnaire in September-October 2020. Methods: The questionnaire included self-reported measures of online health information literacy, COVID-19 knowledge, and anxiety along with exercises on searching and evaluating COVID information online. Results: Students reported moderate levels of information literacy (76%, 3.8 out of 5), but did not perform as well while searching the Internet for COVID-19 information (average accuracy: 65%, 10.5 out of 16 points). Students with higher Internet exercise scores reported higher GPA, higher household income, and liberal political views, and were more likely to follow public health measures [ie, social distancing, mask wearing, and not self-medicating with (hydroxy)chloroquine]. Conclusions: University students may overestimate their information literacy while struggling with searching the Internet for accurate COVID-19 information.

Associations between digital literacy, health literacy, and digital health behaviors among rural residents: evidence from Zhejiang, China

OBJECTIVE

Within the digital society, the limited proficiency in digital health behaviors among rural residents has emerged as a significant factor intensifying health disparities between urban and rural areas. Addressing this issue, enhancing the digital literacy and health literacy of rural residents stands out as a crucial strategy. This study aims to investigate the relationship between digital literacy, health literacy, and the digital health behaviors of rural residents.

METHODS

Initially, we developed measurement instruments aimed at assessing the levels of digital literacy and health literacy among rural residents. Subsequently, leveraging micro survey data, we conducted assessments on the digital literacy and health literacy of 968 residents in five administrative villages in Zhejiang Province, China. Building upon this foundation, we employed Probit and Poisson models to empirically scrutinize the influence of digital literacy, health literacy, and their interaction on the manifestation of digital health behaviors within the rural population. This analysis was conducted from a dual perspective, evaluating the participation of digital health behaviors among rural residents and the diversity to which they participate in such behaviors.

RESULTS

Digital literacy exhibited a notably positive influence on both the participation and diversity of digital health behaviors among rural residents. While health literacy did not emerge as a predictor for the occurrence of digital health behavior, it exerted a substantial positive impact on the diversity of digital health behaviors in the rural population. There were significant interaction effects between digital literacy and health literacy concerning the participation and diversity of digital health behaviors among rural residents. These findings remained robust even after implementing the instrumental variable method to address endogeneity issues. Furthermore, the outcomes of robust analysis and heterogeneity analysis further fortify the steadfastness of the aforementioned conclusions.

CONCLUSION

The findings suggest that policymakers should implement targeted measures aimed at enhancing digital literacy and health literacy among rural residents. This approach is crucial for improving rural residents' access to digital health services, thereby mitigating urban-rural health inequality.

Factors Associated with the e-Health Literacy Among Older Adults with Chronic Obstructive Pulmonary Disease: A Cross-Sectional Study

Background: The telemanagement model in chronic diseases needs older patients to have a certain level of e-Health literacy. According to Electronic Health Literacy model, factors associated with the e-Health literacy among older patients could be comprehensively investigated from individual, situational, and environmental aspects. Objectives: To investigate the e-Health literacy levels among older patients with chronic obstructive pulmonary disease (COPD) and explore associated factors. Methods: A cross-sectional study was conducted among older patients with COPD. The e-Health Literacy Scale was used to measure individuals' e-Health literacy. The multiple linear regression was applied to identify factors associated with e-Health literacy. Results: A total of 230 responses were included in the final analysis. The average score of e-Health literacy for older COPD patients was 24.66 (6.86). After adjusting the model, the results of multiple linear regression demonstrated that aging attitudes (B = 0.067, p < 0.001), technophobia (B = -0.285, p < 0.001), and self-efficacy (B = 0.431, p < 0.001) accounted for 68.3% (p < 0.001) of the total variation in e-Health literacy. Conclusion: This study identifies significant correlations of technophobia, aging attitudes, and self-efficacy, respectively, with e-Health literacy, and self-efficacy and technophobia may be constant predictive factors of e-Health literacy. In the future, intervention research on e-Health literacy should be conducted from a social psychology perspective, with particular emphasis on addressing negative aging attitudes and technophobia. That will promote the tele-management model of chronic diseases. Trial Registration: Chinese Clinical Trial Registry (ChiCTR): ChiCTR1900028563; http://apps.who.int/trialsearch/default.aspx.

Nomophobia and eHealth literacy among adolescents: A cross-sectional study

PURPOSE

This study was conducted to determine adolescents' nomophobia behaviors, eHealth literacy levels, and their predictive factors.

DESIGN AND METHODS

A cross-sectional design was used. The population consisted of ninth, 10th, and 11th-grade adolescents (aged 14-17) in a high school in Turkiye (N = 1013). The study was completed with 719 voluntary adolescents. A questionnaire, the eHealth Literacy Scale, and the Nomophobia Questionnaire were used to collect data. Multiple linear regression was run for data analysis.

RESULTS

Of the adolescents, 44.9% were found to have moderate, and 14.8% had severe nomophobia behaviors. Adolescents who were girls, the ninth and 10th graders, who used smartphone two hours or more daily, social media users, who watched videos on the smartphone and surfed on the Internet had higher nomophobia behaviors. Adolescents who were boys, who searched for health information online, found health information on the Internet helpful, and found accessing health resources on the Internet important had higher eHealth literacy levels.

CONCLUSION

Adolescents should be educated about rational smartphone and Internet usage to comprehend the accuracy of health-related information.

PRACTICE IMPLICATIONS

School nurses could design activities to increase eHealth literacy to encourage adolescents. Nurses could help proactively prepare nomophobia severity map and an eHealth literacy enhancement curriculum for adolescents to form functional preventive interventions. Nurse leaders can contribute to developing policies and strategies to prevent nomophobia behaviors among adolescents.

Are health promotion activities and preventive health practices predictors of adolescent eHealth literacy?

Adolescents' general health status and health behaviors have been closely monitored in recent years. It is estimated that adolescents comprise one-sixth of the world's population. This study examined whether eHealth literacy was predicted via health promotion activities and preventive health practices among Turkish adolescents. This cross-sectional study was conducted between March and May 2022 with 706 adolescents in Çorum, Turkey. A face-to-face questionnaire form was used, including socio-demographic characteristics, preventive health practices, eHealth literacy scale and Adolescent Health Promotion Scale. The data of the research were analyzed with the SPSS 22.0 program. Percentage, mean, Pearson Correlation analysis, and multiple regression analysis were used in the analyzes. The p < 0.05 value was considered statistically significant in the evaluations. In the study, 55.8% were female, and the mean age of the whole group was 16.09 ± 2.63 years. The mean score on the eHealth literacy scale was 29.40 ± 6.29. The mean of the total scores obtained from the Adolescent Health Promotion Scale was 137.97 ± 21.87. There was a significant positive correlation between eHealth literacy and the Adolescent Health Promotion Scale (p < 0.001). Multiple linear regression analysis was performed to predict eHealth literacy using the variables of Adolescent Health Promotion and preventive health practices. eHealth literacy positively and significantly predicts health promotion activities (β = 0.27, t (695) = 7.54, p < 0.001). eHealth literacy increased annual weight measurement by more than 0.13 (β = 0.13), annual blood iron measurement by more than 0.16 (β = 0.16) and annual dental examination by more than 0.11 (β = 0.11). Developing eHealth literacy interventions will be necessary for environments with a high concentration of adolescents (schools and courses). Also, primary health care services should be integrated with the school environment.

The Relationship Between eHealth Literacy and Mental Health of Adult Workers: A Cross-Sectional Study

The current cross-sectional study aimed to investigate eHealth literacy among adult workers in China and explore the relationship between eHealth literacy and mental health in this population. Convenience sampling was used to conduct a survey among adult workers aged 18 to 60 years. Potential participants were chosen from those who completed physical examinations at the Health Management Center of one hospital in China. Participants' eHealth literacy and mental health were assessed using the eHealth Literacy Scale, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Perceived Stress Scale. Mean scores for eHealth literacy and perceived stress were 28.39 (SD = 5.78) and 5.49 (SD = 2.61), respectively. Depression and anxiety were found in 8.2% and 10.1% of participants, respectively. Multivariate logistic regression analysis showed that a 1-point increase in eHealth literacy score was associated with a 7% decrease in depressive symptoms (odds ratio [OR] = 0.93, 95% confidence interval [CI] [0.88, 0.99]) and a 6% decrease in anxiety symptoms (OR = 0.94, 95% CI [0.89, 0.99]). Furthermore, multiple linear regression analysis showed that perceived stress decreased 0.08 points (B = -0.08, 95% CI [-0.13, -0.04]) with every 1-point increase in eHealth literacy score. Thus, eHealth literacy has the potential to reduce risk of depression, anxiety, and perceived stress, and to promote the mental health of adult workers. [Journal of Psychosocial Nursing and Mental Health Services, 61(9), 40-46.].

Health literacy among adolescents and young adults in the Eastern Mediterranean region: a scoping review

OBJECTIVES

For adolescents and young adults, most health literacy research has been conducted in Western countries, but few studies have been conducted in the Eastern Mediterranean region (EMR). This review aimed to explore the existing health literacy research in the EMR in addition to the levels of health literacy and its associated factors among adolescents and young adults.

METHODS

The search, conducted using the PubMed/MEDLINE, EBSCOhost/CINAHL plus, Web of Science and J-STAGE databases, was initially performed on 16 June 2022 and later updated on 1 October 2022. Studies that targeted 10-25 years old persons, conducted in any of the EMR countries and that used the health literacy concept and/or described its levels or predictors were included in the review. Content analysis was used for data extraction and analysis. Data related to the study methods, participants, outcome variables and health literacy were extracted.

RESULTS

The review included 82 studies, most of which were conducted in Iran and Turkey and adopted a cross-sectional design. Half of the studies showed that more than half of adolescents and young adults had low or moderate health literacy. Nine studies applied university-based or school-based health education interventions to improve health literacy, which was also predicted by demographic and socioeconomic factors and internet usage. Little attention was paid to assessing the health literacy of vulnerable people, such as refugees and those with a disability and exposed to violence. Finally, various health literacy topics were investigated, including nutrition, non-communicable diseases, media and depression.

CONCLUSION

Health literacy levels were low-to-moderate in adolescents and young adults in the EMR. To promote health literacy, it is recommended to use school-based health education and attempt to reach adolescents and young adults through social media platforms. More attention should be paid to refugees, people with disabilities and those exposed to violence.

Factors influencing the e-health literacy in cancer patients: a systematic review

PURPOSE

As Internet technology evolves, electronic health (e-health) literacy gradually becomes a key factor in healthy behaviors and health-related decision-making. However, little is known about the influencing factors of e-health literacy among cancer survivors. Thus, the objective of this study was to systematically review the status quo, assessment tools, and influencing factors of e-health literacy in cancer patients.

METHODS

We conducted a comprehensive search in several databases, including PubMed, MEDLINE, Embase, CINAHL, PsycInfo, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, Chinese BioMedical Literature Database, and Chinese Science and Technology Journal Database between January 2000 and December 2021.

RESULTS

A total of nine articles were included in this review, all of which were cross-sectional studies. Following the JBI critical appraisal tool, seven of them were rated as high quality. The e-Health Literacy Scale (eHEALS) was the most commonly used measurement for e-health literacy in cancer patients. The level of e-health literacy in cancer survivors was not high, which was associated with a variable of factors. The behavioral model of health services use was adopted to summarize related influencing factors. From an individual's perspective, predisposing characteristics and enabling resources were the most significant factors, without factors related to needs characteristics.

CONCLUSION

The study has identified the influencing factors of e-health literacy among cancer survivors, including age, gender, domicile place, education level, information-seeking behavior, and social support. In the future, e-health literacy lectures need to be carried out for elderly cancer patients, especially those who live in rural areas and have no access to the Internet. Families and friends of cancer survivors should also be encouraged to offer them more support.

IMPLICATIONS FOR CANCER SURVIVORS

These findings of this review provide novel insights for both family members and medical workers to improve e-health literacy in cancer patients. Further research is required to develop easy-to-use electronic health information acquisition devices and establish propagable e-health literacy intervention programs for cancer survivors.

Cut-off value of the eHEALS score as a measure of eHealth skills among rural residents in Gansu, China

Background

In this study, our aim was to quantify eHealth literacy and determine a cut-off value for eHEALS scores that signifies adequate eHealth literacy among rural residents in Gansu, China.

Methods

This cross-sectional investigation encompassed 18 rural areas across three cities-Lanzhou, Wuwei, and Dingxi-in Gansu. A total of 451 residents were recruited and underwent evaluations for both eHealth literacy and eHealth skills, utilizing the eHEALS and self-constructed tasks, respectively. A receiving operator characteristic curve was plotted with eHealth skills as the dependent variable and eHealth literacy levels as the independent variable, aiming to determine a cut-off value for eHEALS indicating adequate eHealth literacy and evaluate its predictive capacity.

Results

Among the 451 respondents, 10.9% did not possess a personal electronic device with Internet access, while 6.4% owned but had never used them. Within the remaining 373 residents, the mean eHealth literacy score was 25.85 (SD:10.93), item scores ranged from 3.12 (SD:1.45) to 3.42 (SD:1.60). Completion rates for three eHealth skills varied from 39.1% to 59.8%. The cut-off value was 29.5 determined by the Youden index. The area under the receiver operating curve was 0.829, with a sensitivity of 86.7%, and a specificity of 66.8%.

Conclusions

Our findings emphasized that eHealth literacy among rural residents remains at a low level. Moreover, we identified a cut-off value of 29.5 for eHEALS scores that signifies adequate eHealth literacy within this demographic.

How to predict the electronic health literacy of Chinese primary and secondary school students?: establishment of a model and web nomograms

Background

The internet has become an important resource for the public to obtain health information. Therefore, the ability to obtain and use such resources has become important for health literacy. This study aimed to establish a prediction model of Chinese students’ electronic health literacy (EHL) to guide government policymaking and parental interventions, identify the predictors of EHL in Chinese students using random forests, and establish a corresponding prediction model to help policymakers and parents determine whether primary and secondary school students have high EHL.

Methods

This is a cross-sectional study. From June to August 2021, a cluster sample survey was conducted with 1,300 students from seven primary and secondary schools in Shaanxi Province, China. We evaluated 1,235 primary and secondary school students using the e-health literacy scale. The data were divided into training and testing datasets in a 70:30 ratio for further analysis using random forest. The predictive accuracy of the score was measured using the area under the receiver operating characteristic curve. We also used decision curve analysis to determine the usefulness of the prediction model by quantifying the net benefits at different threshold probabilities in the validation dataset. 

Results

We found that 33.6% of students had high EHL. The univariate analysis showed that age (P < 0.001), grade (P < 0.001), employment status (P < 0.001), household location (P < 0.001), parental phubbing behavior (P < 0.001), and general self-efficacy (P < 0.001) were significantly associated with EHL. A random forest classification model was developed with the training dataset (872 students), and seven variables were confirmed as important: age, grade, employment status, father education level, game time, parental phubbing behavior, and general self-efficacy. The validation of the model showed good discrimination, with an area under the curve of 0.975 in the training dataset and 0.738 in the testing dataset. The model was translated into an online risk calculator, which is freely available (https://xietao.shinyapps.io/DynNomapp/).

Conclusions

In this study, an intuitive tool to predict the EHL of Chinese primary and secondary school students was developed and validated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13421-4.