This paper is in the following e-collection/theme issue:

Nursing Education and Training (133) JMIR Theme Issue: COVID-19 Special Issue (2461) eHealth Literacy / Digital Literacy (396)

Published on in Vol 5, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39866, first published .
Digital Health Literacy During the COVID-19 Pandemic Among Health Care Providers in Resource-Limited Settings: Cross-sectional Study

Digital Health Literacy During the COVID-19 Pandemic Among Health Care Providers in Resource-Limited Settings: Cross-sectional Study

Digital Health Literacy During the COVID-19 Pandemic Among Health Care Providers in Resource-Limited Settings: Cross-sectional Study

Authors of this article:

Mohammedjud Hassen Ahmed1 Author Orcid Image ;   Habtamu Alganeh Guadie2 Author Orcid Image ;   Habtamu Setegn Ngusie1 Author Orcid Image ;   Gizaw Hailiye Teferi3 Author Orcid Image ;   Monika Knudsen Gullslett4 Author Orcid Image ;   Samuel Hailegebreal5 Author Orcid Image ;   Mekonnen Kenate Hunde6 Author Orcid Image ;   Dereje Oljira Donacho7 Author Orcid Image ;   Binyam Tilahun8 Author Orcid Image ;   Shuayib Shemsu Siraj9 Author Orcid Image ;   Gebiso Roba Debele9 Author Orcid Image ;   Mohammedamin Hajure10 Author Orcid Image ;   Shegaw Anagaw Mengiste11 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

    Original Paper

    1Department of Health Informatics, College of Public Health and Medical Sciences, Mettu University, Mettu, Ethiopia

    2School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia

    3Department of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia

    4Faculty of Health and Social Science, University of South-East Norway, Drammen, Norway

    5Department of Health Informatics, College of Medicine and Health Science, Arba Minch University, Arba Minch, Ethiopia

    6Department of Social Sciences, Mettu University, Mettu, Ethiopia

    7Department of Health Informatics, Mettu University, Mettu, Ethiopia

    8Department of Health Informatics, University of Gondar, Gondar, Ethiopia

    9Department of Public Health, Mettu University, Mettu, Ethiopia

    10Department of Psychiatry, Mettu University, Mettu, Ethiopia

    11Department of Management Information Systems, University of South East Norway, Drammen, Norway

    Corresponding Author:

    Shegaw Anagaw Mengiste, PhD

    Department of Management Information Systems

    University of South East Norway

    Dalegårdsveien 24 C

    Drammen, 3028

    Norway

    Phone: 47 31009832

    Email: Shegaw.Mengiste@usn.no


    Background: Digital health literacy is the use of information and communication technology to support health and health care. Digital health literacy is becoming increasingly important as individuals continue to seek medical advice from various web-based sources, especially social media, during the pandemics such as COVID-19.

    Objective: The study aimed to assess health professionals’ digital health literacy level and associated factors in Southwest Ethiopia in 2021.

    Methods: An institution-based cross-sectional study was conducted from January to April 2021 in Ethiopia. Simple random sampling technique was used to select 423 study participants among health professionals. SPSS (version 20) software was used for data entry and analysis. A pretested self-administered questionnaire was used to collect the required data. Multivariable logistic regression was used to examine the association between the digital health literacy skill and associated factors. Significance value was obtained at 95% CI and P<.05.

    Results: In total, 401 study subjects participated in the study. Overall, 43.6% (n=176) of respondents had high digital health literacy skills. High computer literacy (adjusted odds ratio [AOR] 4.43, 95% CI 2.34-5.67; P=.01); master’s degree and above (AOR 3.42, 95% CI 2.31-4.90; P=.02); internet use (AOR 4.00, 95% CI 1.78-4.02; P=.03); perceived ease of use (AOR 2.65, 95% CI 1.35-4.65; P=.04); monthly income of >15,000 Ethiopian birr (>US $283.68; AOR 7.55, 95% CI 6.43-9.44; P<.001); good knowledge of eHealth (AOR 2.22, 95% CI 1.32-4.03; P=.04); favorable attitudes (AOR 3.11, 95% CI 2.11-4.32; P=.04); and perceived usefulness (AOR 3.43, 95% CI 2.43-5.44; P=.02) were variables associated with eHealth literacy level.

    Conclusions: In general, less than half of the study participants had a high digital health literacy level. High computer literacy, master’s degree and above, frequent internet use, perceived ease to use, income of >15,000 Ethiopian birr (>US $283.68), good knowledge of digital health literacy, favorable attitude, and perceived usefulness were the most determinant factors in the study. Having high computer literacy, frequent use of internet, perceived ease of use, perceived usefulness, favorable attitude, and a high level of education will help to promote a high level of digital health literacy.

    JMIR Nursing 2022;5(1):e39866

    doi:10.2196/39866

    Keywords

    digital, health; literacy; COVID-19; professionals; Ethiopia; health professionals; digital literacy; skills; knowledge; perception; use; education; training 


    Information and communication technologies (ICTs) greatly reduce health disparities through promoting health, preventing disease, and supporting clinical care for all [1-3]. Moreover, the development of ICTs for patient and consumer health apps has been exploding in the past decade, with thousands of websites, hundreds of mobile apps, and dozens of special purpose devices targeted at the health care markets [4-6]. These ICTs enhance the digitalization of health and diversify the use of digital health around the globe; ultimately, it leads to the accessibility of high-quality, cost-effective health care service delivery through improving the communication of health professionals [7-11].

    Public health emergencies such as COVID-19 need up-to-date health information for prevention, tackling of the disease, and the protection of the community from long-lasting economic and societal impacts. Digital health is the use of emerging ICT, mainly the internet, to improve health that emphasizes the roles that digital technologies play in facilitating health care, health information delivery and storage, and health-related social support [7,12,13]. However, electronic health tools provide little value if the intended actors of health care systems, such as patients and health professionals, lack the skills to engage them effectively. This makes the skills to search, select, appraise, and apply web-based health information and health care–related digital apps increasingly important in the health care area [5,14,15]. These skills are called digital health literacy or eHealth literacy [16-18].

    Digital health literacy is a congregate set of 6 basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy) [15]. On the other hand, digital health literacy not only requires the ability to search for health-related information, understand the information, and apply it appropriately but also indicates advanced technology that involve patient empowerment and involvement, sharing information, and social networking [19,20]. In the context of digitization, it needs to be emphasized that users are not just passive recipients but rather active participants in the communication process by interacting with existing content or by sharing their own health-related information [21]. In this regard, health care professionals should be able to identify and use reliable health care information sources from the internet and other relevant sources of information to make evidence-based medical decisions as well as to improve health care service delivery [10,15,22,23].

    Ethiopia is at a pivotal moment in its efforts to strengthen the health status of its population. As Ethiopia has made progress in reaching the health-related Millennium Development Goals, the government has realized that these advances need to be accelerated if targets in the areas of maternal and child mortality and infectious diseases are to be achieved.

    Even the interaction between technology and health care has a long history, as the embracing of digital health is slow because of limited infrastructural arrangements, capacity, and political willingness [7]. Regardless of the escalating number of internet users and mobile phone penetration around the globe, the implementation of digital health systems continues to be challenging, especially in resource-limited countries [24-26]. Ethiopia is in the process of putting in place a digital health program to improve the delivery of health care services. In line with this, the Ethiopian government has implemented a strategy that focuses on digitalizing the health system [27-30]. However, the low internet penetration in Ethiopia—less than 2%, [31]—and the skills needed to find and evaluate web-based resources remain a challenge for the sustainability of digital health programs [32].

    Literature has depicted that digital health literacy positively influences health-promoting behaviors and people’s health-related quality of life. Digital health literacy is also influenced by educational background, motivation for seeking the information, the technologies used, frequent internet use, computer literacy, digital health training, knowledge regarding the availability and importance of health information, perceived usefulness, having higher internet efficacy, and attitude toward using web-based health information resources [32-40]. Most of the previous studies conducted in Ethiopia did not examine the potential factors of digital health literacy skill. Some of them focused on digital health strategies, web-based health information source, and the application of ICT and use of computer in the health care area.

    Addressing these problems will have a practical benefit for improving the quality of health and health care services. Moreover, evaluating health professionals’ digital health literacy skills would allow the government to identify a variety of literacy levels and hindering factors to generate a proper response accordingly. Therefore, this study was aimed at assessing the digital health literacy skill and associated factors among health professionals working at public health facilities in the Illubabor and Buno Bedele zones, Ethiopia.


    Study Design and Setting

    The cross-sectional study was conducted from January to April 2021 at selected public hospitals in the Illubabor and Buno Bedele zones, Oromia Regional state, Ethiopia. Currently, the Illubabor zone has 14 woredas and 1 administrative town. as well as 41 health centers and 2 hospitals (1 referral hospital and 1 primary hospital). Mettu Karl Referral Hospital and Darimu Hospital provide primary and advanced health care service for the Illubabor zone. The Buno Bedele zone has 10 woredas and 1 administrative town, as well as 3 hospitals called Buno Bedele General Hospital, Dambi Hospital, and Chora Hospital. The health systems of both zones include hospitals, health centers, and health posts.

    Population

    The source population were all health professionals working at the public hospitals of the 2 zones [41]. All selected health professionals working at the public health hospitals of the 2 zones and available during data collection time were included in the study. Health professionals who have less than 6 months working experience from the 2 zones were excluded from this study [41].

    Sample Size and Sampling Procedure

    The sample size was calculated using a single population proportion formula by considering the following assumptions.

    Therefore, 423 participants were included for this study.

    Data Collection and Data Quality Control

    The design and development of the self-administered structured questionnaire for this study was guided by literature reviews. Questions were adapted from other studies [17,33,39,42-45]. The questionnaires gathered information about the participants’ sociodemographic characteristics, computer skills, attitude, access, and technology-related factors. Data were collected using a self-administered questionnaire that was prepared in English. A total of 4 degree-holding health professionals and 8 health professionals participated in the data collection process as supervisors and data collectors, respectively.

    To ensure the quality of data, a pretest was conducted at Jimma University, which has a similar population to our study setting, by taking 10% of respondents from the total sample size. Subsequently, the necessary correction was completed based on the pretest finding. The validity of the questionnaire was determined based on the view of experts, and the reliability was obtained by calculating the Cronbach α (.7) [41]. The scale evidenced high internal consistency (overall Cronbach α=.87). Data collecting material was checked for spelling errors and its completeness and code before the actual data collecting date.

    The data were also checked daily by the supervisor and the investigator for its consistency and completeness.

    A 2-day training was given to data collectors about the purpose of the study, the content of the questionnaires, and all the study protocols to be followed throughout the data collection. Health facilities were assigned to each data collector so as to increase the response rate. Supervisors conducted regular supervision. Data backup activities such as storing data in different places and duplicating hard and soft copies of data were performed to prevent data loss. Before running the logistic regression model, assumptions were checked for outliers, multicollinearity, and independent error terms. Multicollinearity was tested by running a false linear regression iterating the independent variables as the independent variable, and the result showed the entire variance inflation factor value as less than 3 and tolerance as greater than 0.7, which demonstrated the absence of multicollinearity [41]. The data were also checked for outliers by a box plot, and no outshining outlier effect was observed. The model’s goodness of fit was also checked.

    We used omnibus tests of model coefficients for the overall (global) fitness of the model and a Hosmer-Lemeshow test for the fitness of the data to the model. Consequently, the omnibus test result was significant with a P value <.05, and the Hosmer-Lemeshow test showed a good model fit with a P value of .61.

    Data Management and Analysis

    The data was entered using Epi Info (version 7; Centers for Disease Control and Prevention), and analysis was done using SPSS (version 20; IBM Corp) software. Frequency and descriptive statistics were used to describe respondents’ characteristics.

    Binary logistic regression analysis was conducted to assess the effect of selected variables on digital health literacy skill. Variables having a P value <.2 on the bivariate analysis were entered into a multivariable logistic regression analysis to check for confounding effects on the association from bivariate analysis. The strength of association was described at 95% CI, and a P value <.05 was considered significant. Odds ratios were used to determine the strength of association. Multicollinearity was checked between independent variables.

    Ethics Approval

    All methods of the study were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the ethical review board of Mettu University (approval ARCSV/161/2013). A permission letter was received from each hospital. After the objective of the study was explained, informed consent was obtained from all study participants. Moreover, privacy and the confidentiality of information were strictly guaranteed by all data collectors and investigators. The information retrieved was used only for the study. Thus, the names of participants and other personal identifiers were not included in the data collection tool.

    Result

    Participants

    In total, 401 study subjects were included in the study. The response rate was 94.8% (401/423). The mean age of the participants was 32.13 (SD 11.2) years. Of the 401 participants, 217 (54.1%) were aged <30 years and 248 (61.8%) were male. Almost half (n=206, 51.4%) of the participants had a monthly income of <5000 Ethiopian birr (<US $94.56), and only 35 (8.7%) had a monthly income of 10,000-15,000 Ethiopian birr (US $189.12-283.68). Regarding education, 119 (54.6%) participants had a diploma and only 46 (11.5%) had a master’s degree and above. Additionally, 211 (52.6%) health professionals had <5 years of working experience and only 48 (12%) had >10 years of working experience. Among the participants, 124 (30.9%) were nurses and 107 (26.7) were physicians, as shown in Table 1.

    Table 1. Sociodemographic characteristics of health professionals.
    Variable, categoryParticipant (N=401), n (%)
    Age (years)

    		<30217 (54.1)

    		30-3994 (23.4)

    		40-4949 (12.2)

    		>4941 (10.2)
    Gender

    		Female153 (38.2)

    		Male248 (61.8)
    Monthly income (Ethiopian birr)

    		<5000 (<US $94.56)206 (51.4)

    		5000-10,000 (US $94.56-189.12)114 (28.4)

    		10,000-15,000 (US $189.12-283.68)35 (8.7)

    		>15,000 (>US $283.68)46 (11.5)
    Educational status

    		Diploma219 (54.6)

    		Bachelor’s degree136 (33.9)

    		Master’s degree and above46 (11.5)
    Experience (years)

    		<5211 (52.6)

    		5-10142 (35.4)

    		>1048 (12)
    Professional category

    		Nurse124 (30.9)

    		Physician107 (26.7)

    		Midwifery98 (24.4)

    		Laboratorian49 (12.2)

    		Others23 (5.7)
    Digital Health Literacy Level

    The median digital health literacy score was 27.4 (SD 8.3). Scores less than the median value were labeled as low digital health literacy level, and scores greater than or equal to the median value were labeled as high digital health literacy level. From the total, 43.6% (175/401; 95% CI: 40.7-54.12) had high digital health literacy skills during the pandemic. Associated factors with a P value <.2 from the bivariate analysis were included in the final multivariable logistic regression model to control the effect of confounding as shown in Table 2.

    Table 2. Digital health literacy level questions among health professionals (N=401).
    ItemsStrongly disagree, n (%)Disagree, n (%)Neutral, n (%)Agree, n (%)Strongly agree, n (%)
    Digital health literacy skills

    		The internet is useful in helping you make decisions about your health123 (30.7)113 (28.2)48 (12)67 (16.7)50 (12.5)

    		The internet is important for you to be able to access health resources121 (30.2)115 (28.7)45 (11.2)71 (17.7)49 (12.2)

    		I know what COVID-19–related health resources are available on the internet43 (10.8)55 (13.8)81 (20.2)121 (30)101 (25.2)

    		I know where to find helpful health resources on the internet41 (10.2)47 (11.7)91 (22.7)119 (29.7)103 (25.7)

    		I know how to find helpful COVID-19 pandemic resources on the internet6 (1.4)56 (14)78 (19.5)181 (45.1)80 (20)

    		I know how to use the internet to answer my questions about the COVID-19 pandemic4 (1)52 (13)75 (18.7)179 (44.6)91 (22.7)

    		I know how to use the health information about the COVID-19 pandemic I find on the internet to help me8 (2)54 (13.5)70 (17.4)175 (43.6)94 (23.5)

    		I have the skills I need to evaluate the COVID-19–related resources I find on the internet11 (2.7)48 (12)69 (17.2)172 (42.9)101 (25.2)

    		I feel confident in using information from the internet to make COVID-19–related decisions8 (2)70 (17.5)54 (13.5)147 (36.6)122 (30.4)
    Internet Use

    Overall, of the 401 respondents, 49.3% (n=198) reported that they used the internet and 203 (50.6%) reported that they have never used the internet. Of the 198 internet users, about one-half (n=99, 50%) accessed the internet or email on a daily basis. Most (304/401, 75.8%) health professionals had access to the internet from home.

    Factors Associated With Digital Health Literacy Level

    All variables were entered into the binary logistic regression model. Computer literacy, marital status, educational status, monthly income, place of residence, self-efficacy, perceived ease of use, perceived usefulness, attitude and knowledge, and the frequency of internet use were significant factors associated with digital health literacy from the bivariable analysis. All variables were entered into the multivariable logistic regression model. Computer literacy, educational status, monthly income, place of residence, self-efficacy, perceived ease of use, perceived usefulness, attitude and knowledge, and the frequency of internet use were significant factors associated with eHealth literacy from the multivariable analysis. Accordingly, those having high computer literacy were 4.43 (95% CI 2.34-5.67; P=.01) times more likely to have a high eHealth literacy level than those who have low computer literacy. Similarly, respondents who have a master’s degree and above were 3.42 (95% CI 2.31-4.90; P=.02) times more likely to have a high eHealth literacy level than those who have a bachelor’s degree or diploma. Health professionals who used the internet daily were 4.00 (95% CI 1.78-4.02; P=.03) times more likely have a high eHealth literacy level than those who used less than 1 day per week. Similarly, respondents who perceived eHealth as being easy to use were about 2.65 (95% CI 1.35-4.65; P=.04) times more likely to have a high eHealth literacy level than respondents who perceived eHealth as not being easy to use. Respondents who earn a monthly income of >15,000 Ethiopian birr (>US $283.68) were 7.55 (95% CI 6.43-9.44; P<.001) times more likely to have a high eHealth literacy level than respondents who received income of <15,000 Ethiopian birr (<US $283.68). Those who have good knowledge of eHealth were 2.22 (95% CI 1.32-4.03; P=.04) times more likely to have a high eHealth literacy level than respondents with low knowledge of eHealth. Attitude was also found to be a significant factor that affected the level of eHealth literacy; respondents with favorable attitudes about eHealth were about 3.11 (95% CI 2.11-4.32; P=.04) times more likely to have a high level of eHealth literacy than health professionals who had unfavorable attitude toward eHealth. Additionally, health professionals who perceived usefulness were about 3.43 (95% CI 2.43-5.44; P=.02) times more likely to have a high eHealth literacy level than respondents who did not perceived usefulness, as shown in Table 3.

    Table 3. Bivariable and multivariable analysis of factors associated with digital health literacy among health professionals.
    VariablesDigital literacy level, nCORa (95% CI)AORb (95% CI)P value
    HighLow
    Computer literacy

    		High computer literacy84885.10 (2.43-7.65)4.43 (2.34-5.67).01

    		Low computer literacy1903911
    Marital status

    		Married211981.40 (1.12-2.99)1.21 (0.98-2.31).07

    		Not married692311
    Educational Status

    		Diploma1457411

    		Bachelor’s degree101351.50 (1.21-3.4)1.49 (0.97-2.54).11

    		Master’s degree and above4154.18 (2.51-6.54)3.42 (2.31-4.90).02
    Monthly income (Ethiopian birr)

    		<5000 (<US $94.56)5015611

    		5000-10,000 (US $94.56-189.12)56583.01 (2.11-5.04)1.90 (0.96-4.11).09

    		10,000-15,000 (US $189.12-283.68)19163.70 (3.21-5.03)2.96 (2.55-4.04).01

    		>15,000 (>US $283.68)34128.84 (5.44-11.65)7.55 (6.43-9.44)<.001
    Frequency of internet use

    		Less than 1 day per week 5510111

    		Several days per week67482.56 (1.89-3.94)2.31 (1.76-3.88).03

    		Daily76542.58 (1.81-3.81)4.00 (1.78-4.02).03
    Knowledge

    		Good knowledge190562.39 (1.51-4.80)2.22 (1.32-4.03).04

    		Poor knowledge916411
    Attitude

    		Favorable attitude210563.38 (2.41-4.80)3.11 (2.11-4.32).04

    		Unfavorable attitude716411
    Perceived ease of use

    		Easy147802.73 (1.51-4.74)2.65 (1.35-4.65).04

    		Not easy7010411
    Perceived usefulness

    		Useful158694.35 (2.76-6.89)3.43 (2.43-5.44).02

    		Not useful6011411

    aCOR: crude odds ratio.

    bAOR: adjusted odds ratio.


    Principal Findings

    This study attempted to describe and assess the digital health literacy of health professionals and significant factors. Digital health literacy is the major barrier to access updated health information for health professionals, specifically during public health emergencies. Overall, the findings from this study suggested that the digital health literacy level was low (43.6%; 95% CI 40.7-54.12), which was consistent with previous findings [32,38,46-48]. At the same time, our result was lower than the study findings in the Netherlands (76%) [17], Pakistan (54.3%) [42], and Iran (54.4%) [48]. Likewise, a study in Chicago reported that one-quarter of health professionals had low digital health literacy [49]. This variation could be due to the fact that our study was focused on the resource-limited country setting of Ethiopia, in which the internet penetration was very low. Surprisingly, our finding was lower than those of the studies conducted in Northwestern Ethiopia, which reported that 60% [36] and 69.3% [50] of respondents possessed high digital literacy. This could be due to infrastructure differences in the selected health care facilities.

    In contrast, our finding was higher than that of the study conducted in Korea, where digital health literacy was 38.8% [51]. These different findings may be related to the difference between the target populations of these studies. In this study, the participants were health professionals, whereas the study conducted in Korea was among nursing students.

    Digital health literacy level is interlinked with sociodemographic, behavioral, and technological factors. Our finding implies that the computer literacy level of health professionals had a direct relationship with digital health literacy level. The professionals who had a high computer literacy level were 4.43 (95% CI 2.34-5.67) times more likely to have higher digital health literacy, which was supported by a previous study [52]. This finding was due to the fact that computer literacy, which is the knowledge and ability to use computer-related technology, made the interaction of health professionals with digital health applications easier.

    Professionals who had a master’s degree were 3.42 (95% CI 2.31-4.90) times more likely to have a high level of digital health literacy than health professionals who were only diploma holders. This finding is supported by studies conducted elsewhere [38,45,53] and could be due to the fact that higher education makes one more proficient with digital tools use and web-based resources. This finding strengthens the concept that higher education is interlinked with higher use of the internet for health purposes [54].

    Similar to previous finding elsewhere [33,38,50,55,56], this study revealed that health professionals who had a higher monthly income were more likely to have a high digital health literacy level, which was 2.96 (95% CI 2.55-4.04) and 7.55 (95% CI 6.43-9.44) times higher for health professionals who had a monthly income of 10,000-15,000 Ethiopian birr (US $189.12-283.68) and >15,000 Ethiopian birr (>US $283.68) than health professionals who earned <5,000 Ethiopian birr (<US $94.56), respectively. This finding might be due to high-earning health professionals having the necessary digital tools such as computer, smart phone, and tablets. However, this study was in contrast with a previous study in Northwest Ethiopia, which reported that a higher monthly income lowers digital health literacy level [32]. This difference might be due to the study setting and participants’ sociodemographic characteristics.

    Health professionals who used the internet daily were 4.00 (95% CI 1.78-4.02) times more likely to have digital health competency than those who did not use the internet at least one day per week. This finding is in line with previous studies conducted in different areas [38,49,52,57,58] and could be due to the fact that the internet is the precondition for using digital health tools.

    The result of this study indicates that health professionals who were knowledgeable on health information sources were about 2.22 (95% CI 1.32-4.03) times more likely to have higher digital health literacy than who had poor knowledge, and this result was supported by previous studies [50,59,60]. The possible explanation for this finding could be that digital health–related knowledge builds the competency and skill for using web-based health information sources, and knowledgeable health professionals can look up what and how to do a skill or task.

    Health professionals who had a favorable attitude were 3.11 (95% CI: 2.11, 4.32) times more likely to have higher digital health literacy. This result was consistent with previous studies [37,38,50,60]. The explanation for this result could be that the attitude of health professionals helps them be more committed, since they do not consider it to be wasting their time when using digital health tools. Having a favorable attitude indicates an understanding of the relevance and use of digital health tools that could lead to a high literacy level by creating motivated health professionals. Moreover, the change in attitude might lead the overall technological and cultural change.

    Regarding the perceived ease of use, this study implied that health professionals who perceived using digital health tools as being easy were 2.65 (95% CI 1.35-4.65) times more likely to have a higher digital health literacy level than their counterparts. This finding could be due to the fact that health professionals who consider using digital tools as being easy were more confident when practicing and building their literacy, and it is known that the perceived ease of use could influence health professionals’ acceptance of digital health information technologies [61].

    Health professionals who perceive digital tools as useful were 3.43 (95% CI 2.43-5.44) times higher in digital health literacy. This finding is in line with a previous study in Northwest Ethiopia [36] and might be due to the perceived benefit from using digital health tools that enhanced health professionals’ attitude, which ultimately leads to sustainably practicing the use of the tools.

    Limitations

    First, the study was a facility-based cross-sectional study, which could not be used to identify causal inference. Second, the study was conducted at health facilities and might not be generalizable to all administrations of the country. In addition, the study was not able to include health professionals working at private health facilities. Finally, we recommend repeating our study in different parts of the country to determine the level of eHealth literacy, including health professionals from private hospitals.

    Conclusions

    In general, less than half of the study participants had a high digital health literacy level. High computer literacy, master’s degree and above, frequent internet use, perceived ease of use, monthly income of >15,000 Ethiopian birr (>US $283.68), good knowledge of digital health, favorable attitudes, and perceived usefulness were the most determinant factors associated with digital health literacy skills. Having a high computer literacy, frequent use of internet, perceived ease of use, perceived usefulness, favorable attitude, and high level of education will help promote the level of digital health literacy. However, the level of digital health literacy among health professionals in this study area was relatively low. Thus, an attempt needs to be taken to fill the gap in digital health literacy among health professionals that will help them increase their productivity and increase the relevance of digital health to their day-to-day tasks.

    Acknowledgments

    The authors would like to thank the Mettu University, College of Health Science for the approval of ethical clearance and health facilities and data collectors, supervisors, and study participants.

    Data Availability

    The data sets generated and/or analyzed during the current study will be available upon reasonable request from the corresponding author.

    Authors' Contributions

    All authors made substantial contributions to conception and design, the acquisition of data, or the analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

    Conflicts of Interest

    None declared.

    1. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res 2015 Feb 24;17(2):e52 [FREE Full text] [CrossRef] [Medline]
    2. Farach N, Faba G, Julian S, Mejía F, Cabieses B, D'Agostino M, et al. Stories from the field: the use of information and communication technologies to address the health needs of underserved populations in Latin America and the Caribbean. JMIR Public Health Surveill 2015 Mar 17;1(1):e1 [FREE Full text] [CrossRef] [Medline]
    3. Sequist TD, Cullen T, Acton KJ. Indian health service innovations have helped reduce health disparities affecting American Indian and Alaska Native people. Health Aff (Millwood) 2011 Oct;30(10):1965-1973. [CrossRef] [Medline]
    4. Neter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res 2012 Jan 27;14(1):e19 [FREE Full text] [CrossRef] [Medline]
    5. Bickmore TW, Paasche-Orlow MK. The role of information technology in health literacy research. J Health Commun 2012 Oct;17 Suppl 3(sup3):23-29. [CrossRef] [Medline]
    6. Jensen JD, King AJ, Davis LA, Guntzviller LM. Utilization of internet technology by low-income adults: the role of health literacy, health numeracy, and computer assistance. J Aging Health 2010 Sep;22(6):804-826. [CrossRef] [Medline]
    7. Maksimović M, Vujović V. Internet of things based e-health systems: ideas, expectations and concerns. In: Khan S, Zomaya A, Abbas A, editors. Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Cham, Switzerland: Springer; 2017:241-280.
    8. Pohl AL, Trill R. Digital health literacy as precondition for sustainable and equal health care – a study focussing the users' perspective. 2016 Sep 03 Presented at: WIS 2016: Building Sustainable Health Ecosystems; September 16-18, 2016; Tampere, Finland p. 4637-4646. [CrossRef]
    9. Levin-Zamir D, Lemish D, Gofin R. Media Health Literacy (MHL): development and measurement of the concept among adolescents. Health Educ Res 2011 Apr 19;26(2):323-335. [CrossRef] [Medline]
    10. Jackson DN, Trivedi N, Baur C. Re-prioritizing digital health and health literacy in Healthy People 2030 to affect health equity. Health Commun 2021 Sep 30;36(10):1155-1162. [CrossRef] [Medline]
    11. Rouleau G, Gagnon MP, Côté J, Payne-Gagnon J, Hudson E, Dubois CA. Impact of information and communication technologies on nursing care: results of an overview of systematic reviews. J Med Internet Res 2017 Apr 25;19(4):e122 [FREE Full text] [CrossRef] [Medline]
    12. Wentzel MJ, Beerlage-de Jong N, Sieverink F, van Gemert-Pijnen JEWC. Slimmer eHealth ontwikkelen en implementeren met de CeHRes Roadmap. In: Politiek C, Hoogendijk R, editors. Co-Creatie eHealthboek: eHealth, technisch kunstje of pure veranderkunde?. Houten, the Netherlands: Aimpact BV; Dec 11, 2014.
    13. Oh H, Rizo C, Enkin M, Jadad A. What is eHealth?: a systematic review of published definitions. World Hosp Health Serv 2005;41(1):32-40. [Medline]
    14. Park E, Kwon M. Testing the digital health literacy instrument for adolescents: cognitive interviews. J Med Internet Res 2021 Mar 15;23(3):e17856 [FREE Full text] [CrossRef] [Medline]
    15. Norman CD, Skinner H. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res 2006 Jun 16;8(2):e9 [FREE Full text] [CrossRef] [Medline]
    16. Flash Eurobarometer 404. European citizen's digital health literacy: report. European Commission. 2014 Nov.   URL: http://publications.europa.eu/resource/cellar/fd42f9e7-937c-41f3-bf03-4221b2db712b.0001.04/DOC_1 [accessed 2022-10-28]
    17. van der Vaart R, Drossaert C. Development of the Digital Health Literacy Instrument: measuring a broad spectrum of Health 1.0 and Health 2.0 Skills. J Med Internet Res 2017 Jan 24;19(1):e27 [FREE Full text] [CrossRef] [Medline]
    18. Lems WF, Working Group in EULAR EFORt recommendations in patients 50 years and over with a fracture. The rheumatologist-orthopaedic surgeon connexion in secondary fracture prevention: SP0135 The eular/efort recommendations for patients with recent fracture. Ann Rheum Dis 2018 Jun 15. [CrossRef]
    19. Kwon M, Park E. Perceptions and sentiments about electronic cigarettes on social media platforms: systematic review. JMIR Public Health Surveill 2020 Jan 15;6(1):e13673 [FREE Full text] [CrossRef] [Medline]
    20. van de Belt TH, Engelen LJ, Berben SA, Schoonhoven L. Definition of Health 2.0 and Medicine 2.0: a systematic review. J Med Internet Res 2010 Jun 11;12(2):e18 [FREE Full text] [CrossRef] [Medline]
    21. Bittlingmayer UH, Dadaczynski K, Sahrai D, van den Broucke S, Okan O. Digital health literacy-conceptual contextualization, measurement, and promotion. Article in German. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020 Feb 16;63(2):176-184. [CrossRef] [Medline]
    22. Adams SA. Revisiting the online health information reliability debate in the wake of "web 2.0": an inter-disciplinary literature and website review. Int J Med Inform 2010 Jun;79(6):391-400. [CrossRef] [Medline]
    23. Eysenbach G, Diepgen TL. The role of e-health and consumer health informatics for evidence-based patient choice in the 21st century. Clin Dermatol 2001;19(1):11-17. [CrossRef] [Medline]
    24. Olok GT, Yagos WO, Ovuga E. Knowledge and attitudes of doctors towards e-health use in healthcare delivery in government and private hospitals in Northern Uganda: a cross-sectional study. BMC Med Inform Decis Mak 2015 Nov 04;15(1):87 [FREE Full text] [CrossRef] [Medline]
    25. Hoque MR, Bao Y, Sorwar G. Investigating factors influencing the adoption of e-Health in developing countries: a patient's perspective. Inform Health Soc Care 2017 Jan;42(1):1-17. [CrossRef] [Medline]
    26. Shovlin A, Ghen M, Simpson P, Mehta K. Challenges facing medical data digitization in low-resource contexts. 2013 Jan 16 Presented at: 2013 IEEE Global Humanitarian Technology Conference (GHTC); October 20-23, 2013; San Jose, CA p. 365-371. [CrossRef]
    27. Shiferaw F, Zolfo M. The role of information communication technology (ICT) towards universal health coverage: the first steps of a telemedicine project in Ethiopia. Glob Health Action 2012 Apr 02;5:1-8 [FREE Full text] [CrossRef] [Medline]
    28. Sagaro GG, Battineni G, Amenta F. Barriers to sustainable telemedicine implementation in Ethiopia: a systematic review. Telemed Rep 2020 Nov 01;1(1):8-15 [FREE Full text] [CrossRef] [Medline]
    29. Barkman C, Weinehall L. Policymakers and mHealth: roles and expectations, with observations from Ethiopia, Ghana and Sweden. Glob Health Action 2017 Jun 25;10(sup3):1337356 [FREE Full text] [CrossRef] [Medline]
    30. Gebre-Mariam M. Governance lessons from an interorganizational health information system implementation in Ethiopia. E J Info Sys Dev Countries 2018 Aug 06;84(5):e12045. [CrossRef]
    31. Stork C, Calandro E, Gillwald A. Internet going mobile: internet access and use in 11 African countries. Info 2013 Aug 02;15(5):34-51. [CrossRef]
    32. Shiferaw KB, Tilahun BC, Endehabtu BF. Healthcare providers' digital competency: a cross-sectional survey in a low-income country setting. BMC Health Serv Res 2020 Nov 09;20(1):1021 [FREE Full text] [CrossRef] [Medline]
    33. Juvalta S, Kerry MJ, Jaks R, Baumann I, Dratva J. Electronic health literacy in Swiss-German parents: cross-sectional study of eHealth Literacy Scale unidimensionality. J Med Internet Res 2020 Mar 13;22(3):e14492 [FREE Full text] [CrossRef] [Medline]
    34. Holt KA, Karnoe A, Overgaard D, Nielsen SE, Kayser L, Røder ME, et al. Differences in the level of electronic health literacy between users and nonusers of digital health services: an exploratory survey of a group of medical outpatients. Interact J Med Res 2019 Apr 05;8(2):e8423 [FREE Full text] [CrossRef] [Medline]
    35. Thapa S, Nielsen JB, Aldahmash AM, Qadri FR, Leppin A. Willingness to use digital health tools in patient care among health care professionals and students at a university hospital in Saudi Arabia: quantitative cross-sectional survey. JMIR Med Educ 2021 Feb 19;7(1):e18590 [FREE Full text] [CrossRef] [Medline]
    36. Mengestie ND, Yilma TM, Beshir MA, Paulos GK. eHealth literacy of medical and health science students and factors affecting eHealth literacy in an Ethiopian University: a cross-sectional study. Appl Clin Inform 2021 Mar 07;12(2):301-309 [FREE Full text] [CrossRef] [Medline]
    37. Yang E, Chang SJ, Ryu H, Kim HJ, Jang SJ. Comparing factors associated with eHealth literacy between young and older adults. J Gerontol Nurs 2020 Aug 01;46(8):46-56. [CrossRef] [Medline]
    38. Shiferaw KB, Tilahun BC, Endehabtu BF, Gullslett MK, Mengiste SA. E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey. BMC Med Inform Decis Mak 2020 Aug 06;20(1):181 [FREE Full text] [CrossRef] [Medline]
    39. Adil A, Usman A, Jalil A. Qualitative analysis of digital health literacy among university students in Pakistan. J Hum Behav Soc Environ 2020 Dec 08;31(6):771-781. [CrossRef]
    40. Shiferaw KB, Mehari EA, Eshete T. eHealth literacy and internet use among undergraduate nursing students in a resource limited country: a cross-sectional study. Inform Med Unlocked 2020;18:100273. [CrossRef]
    41. Ahmed MH, Bogale AD, Tilahun B, Kalayou MH, Klein J, Mengiste SA, et al. Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model. BMC Med Inform Decis Mak 2020 Sep 03;20(1):207 [FREE Full text] [CrossRef] [Medline]
    42. Zakar R, Iqbal S, Zakar MZ, Fischer F. COVID-19 and health information seeking behavior: digital health literacy survey amongst university students in Pakistan. Int J Environ Res Public Health 2021 Apr 11;18(8):4009 [FREE Full text] [CrossRef] [Medline]
    43. St. Jean B, Greene Taylor N, Kodama C, Subramaniam M. Assessing the digital health literacy skills of tween participants in a school-library-based after-school program. J Consum Health Internet 2017 Mar 10;21(1):40-61. [CrossRef]
    44. Dadaczynski K, Okan O, Messer M, Leung AYM, Rosário R, Darlington E, et al. Digital health literacy and web-based information-seeking behaviors of university students in Germany during the COVID-19 pandemic: cross-sectional survey study. J Med Internet Res 2021 Jan 15;23(1):e24097 [FREE Full text] [CrossRef] [Medline]
    45. Tennant B, Stellefson M, Dodd V, Chaney B, Chaney D, Paige S, et al. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res 2015 Mar 17;17(3):e70 [FREE Full text] [CrossRef] [Medline]
    46. Tariq A, Khan SR, Basharat A. Internet Use, eHealth literacy, and dietary supplement use among young adults in Pakistan: cross-sectional study. J Med Internet Res 2020 Jun 10;22(6):e17014 [FREE Full text] [CrossRef] [Medline]
    47. Park H, Moon M, Baeg JH. Association of eHealth literacy with cancer information seeking and prior experience with cancer screening. Comput Inform Nurs 2014 Sep;32(9):458-463. [CrossRef] [Medline]
    48. KHademian F, Arshadi Montazer MR, Aslani A. Web-based health information seeking and eHealth literacy among college students. a self-report study. Invest Educ Enferm 2020 Feb 26;38(1):e08 [FREE Full text] [CrossRef] [Medline]
    49. Vollbrecht H, Arora VM, Otero S, Carey KA, Meltzer DO, Press VG. Measuring eHealth literacy in urban hospitalized patients: implications for the post-COVID world. J Gen Intern Med 2021 Jan 19;36(1):251-253 [FREE Full text] [CrossRef] [Medline]
    50. Velazquez-Pimentel D, Trockels A, Smith E. Internet use and eHealth literacy among health-care professionals in a resource limited setting: a cross-sectional survey [Letter]. Adv Med Educ Pract 2019 Sep 09;10:803-804 [FREE Full text] [CrossRef] [Medline]
    51. Kim S, Jeon J. Factors influencing eHealth literacy among Korean nursing students: a cross-sectional study. Nurs Health Sci 2020 Sep 24;22(3):667-674. [CrossRef] [Medline]
    52. Tesfa GA, Kalayou MH, Zemene W. Electronic health-information resource utilization and its associated factors among health professionals in Amhara regional state teaching hospitals, Ethiopia. Adv Med Educ Pract 2021 Mar 01;12:195-202 [FREE Full text] [CrossRef] [Medline]
    53. Adil A, Usman A, Khan NM, Mirza FI. Adolescent health literacy: factors effecting usage and expertise of digital health literacy among universities students in Pakistan. BMC Public Health 2021 Jan 09;21(1):107 [FREE Full text] [CrossRef] [Medline]
    54. Xesfingi S, Vozikis A. eHealth literacy: in the quest of the contributing factors. Interact J Med Res 2016 May 25;5(2):e16 [FREE Full text] [CrossRef] [Medline]
    55. Liu P, Yeh L, Wang J, Lee S. Relationship between levels of digital health literacy based on the Taiwan digital health literacy assessment and accurate assessment of online health information: cross-sectional questionnaire study. J Med Internet Res 2020 Dec 21;22(12):e19767 [FREE Full text] [CrossRef] [Medline]
    56. Alanezi F. Factors affecting the adoption of e-health system in the Kingdom of Saudi Arabia. Int Health 2021 Sep 03;13(5):456-470 [FREE Full text] [CrossRef] [Medline]
    57. Wong DK, Cheung M. Online health information seeking and eHealth literacy among patients attending a primary care clinic in Hong Kong: a cross-sectional survey. J Med Internet Res 2019 Mar 27;21(3):e10831 [FREE Full text] [CrossRef] [Medline]
    58. Athanasopoulou C, Välimäki M, Koutra K, Löttyniemi E, Bertsias A, Basta M, et al. Internet use, eHealth literacy and attitudes toward computer/internet among people with schizophrenia spectrum disorders: a cross-sectional study in two distant European regions. BMC Med Inform Decis Mak 2017 Sep 20;17(1):136 [FREE Full text] [CrossRef] [Medline]
    59. Coyne I, Prizeman G, Sheehan A, Malone H, While A. An e-health intervention to support the transition of young people with long-term illnesses to adult healthcare services: design and early use. Patient Educ Couns 2016 Sep;99(9):1496-1504. [CrossRef] [Medline]
    60. Stellefson ML, Shuster JJ, Chaney BH, Paige SR, Alber JM, Chaney JD, et al. Web-based health information seeking and eHealth literacy among patients living with chronic obstructive pulmonary disease (COPD). Health Commun 2018 Dec 05;33(12):1410-1424 [FREE Full text] [CrossRef] [Medline]
    61. Petrič G, Atanasova S, Kamin T. Ill literates or illiterates? investigating the eHealth literacy of users of online health communities. J Med Internet Res 2017 Oct 04;19(10):e331 [FREE Full text] [CrossRef] [Medline]

    ‎
    AOR: adjusted odds ratio
    ICT: information and communication technology

    Edited by E Borycki; submitted 26.05.22; peer-reviewed by S Kanfe, M Liu; comments to author 23.06.22; revised version received 25.06.22; accepted 28.06.22; published 14.11.22

    Copyright

    ©Mohammedjud Hassen Ahmed, Habtamu Alganeh Guadie, Habtamu Setegn Ngusie, Gizaw Hailiye Teferi, Monika Knudsen Gullslett, Samuel Hailegebreal, Mekonnen Kenate Hunde, Dereje Oljira Donacho, Binyam Tilahun, Shuayib Shemsu Siraj, Gebiso Roba Debele, Mohammedamin Hajure, Shegaw Anagaw Mengiste. Originally published in JMIR Nursing (https://nursing.jmir.org), 14.11.2022.

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