Development and validation of an e-health literacy scale for pregnant women

Introduction

Pregnancy duration is counted from the last menstrual period before pregnancy until the fetus and placenta are completely delivered (1). Health management for women is vital for improving pregnancy outcomes and birth quality (2). Kamali’s research shows high demand for information on all pregnancy and childbirth topics among pregnant women (3). E-health literacy is the ability to research, understand, and evaluate health information from electronic resources, and use the obtained information to process and solve health problems (4). With the development of internet-based medicine, e-health literacy has become an important driving factor for individual use of digital health tools. It better reflects the capacity and effectiveness of contemporary pregnant women in health management compared to general health literacy (5). Studies have shown that in the USA (6), 93% of pregnant women actively obtain e-health information through the internet, while 25% of pregnant women search for health information even in the absence of health problems—indicating both widespread use and proactive information-seeking behavior. E-health literacy plays a crucial role in patient health outcomes as one of the modifiable factors within the long-term care process (7). Therefore, e-health literacy has gradually become an important ability indicator for pregnant women.

Pregnancy represents a unique physiological and psychosocial transition, during which women face distinctive health information needs—such as fetal development, nutrition, labor preparation, and postpartum care—that are not fully captured by general e-health literacy tools (8). Moreover, pregnant women increasingly rely on specialized digital platforms and social media (e.g., pregnancy apps, WeChat groups, and online parenting communities) to seek peer support and experiential knowledge, which differ from typical health information sources used by the general population (9). These platforms emphasize social interaction and collective knowledge-sharing, creating a specific e-health environment that requires tailored literacy skills. However, a study has shown that patients with low e-health literacy are more likely to exhibit poor self-management skills—such as medication adherence, appointment keeping, and symptom monitoring—due to difficulties in understanding and applying health information (10). In the context of pregnancy, self-management encompasses a range of behaviors including diet control, physical activity, prenatal self-care, and timely clinical consultation (11). Limited e-health literacy may hinder the acquisition and application of relevant knowledge, thereby compromising self-management and pregnancy outcomes (7). Currently, most studies on e-health literacy in pregnant women have used universal scales that do not incorporate social interaction dimensions, thus failing to accurately reflect the dynamic process of information exchange in maternal health contexts (10). In recent years, social media platforms such as Instagram and WeChat have become popular sources of health information among pregnant women. This shift necessitates that e-health literacy assessments include items related to social interaction and collaborative information evaluation (11). In 2018, Paige et al. (12) proposed an interactive e-health literacy model that incorporates the use of e-health information technology and emphasizes social attributes of e-health engagement. This model addresses the limitations of prior frameworks and aligns with the evolving nature of e-health communication. However, existing e-health literacy scales remain inadequately tailored to the pregnancy population.

Therefore, there is an urgent need to develop a pregnancy-specific e-health literacy assessment tool that accounts for physiological, psychological, and social characteristics of pregnant women. Such a tool would enable targeted health support and contribute to improved e-health literacy and self-management capacity in this group. We present this article in accordance with the TRIPOD reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-48/rc).

Methods

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of Qingdao University (No. QDU-HEC-20221 89) and informed consent was obtained from all individual participants. All participating hospitals were informed of and agreed on the study.

Theoretical framework

This study was guided by the e-Health Literacy Interaction Model (TMeHL), which conceptualizes e-health literacy as comprising four interrelated operational skills: positioning and understanding, communication, evaluation, and application (12). Unlike traditional models such as the eHealth Literacy Scale (eHEALS), which primarily emphasize individual information retrieval and comprehension, TMeHL incorporates the social and interactive dimensions of digital health engagement. This framework is particularly relevant to pregnant women, who frequently utilize social platforms (e.g., pregnancy apps, WeChat groups) for peer support and collective knowledge-sharing—dimensions not adequately addressed by earlier models. Within this framework, we defined four constructs of e-health literacy specific to pregnancy: information acquisition, evaluation, interaction, and application ability.

Literature review

We searched PubMed, Cochrane Library, Web of Science, Embase, EBSCO, Science Direct and Chinese databases such as China Knowledge and Wanfang. The search terms were: “e-health literacy/digital health literacy”, “pregnant women/pregnancy/pregnant”, “scale/scale development”. The publication period was from the inception of each database to February 2024. The inclusion criteria were: studies published in English or Chinese that focused on the development or application of e-health literacy measures in pregnant populations. Conference abstracts, newspapers, magazines, and news articles were excluded. The initial search yielded 1,258 records. After removing duplicates and screening titles and abstracts, 48 full-text articles were assessed for eligibility, of which 22 studies (detailing 15 distinct scales or item pools) were included in the final analysis. Items from these existing scales were extracted and mapped onto the four constructs of the TMeHL framework. This process generated an initial pool of 45 items that covered the theoretical domains but required contextualization to the specific social and interactive behaviors of pregnant women. Items were developed in Chinese and surveys were administered in Chinese.

Qualitative analysis

Semi-structured interviews with ten pregnant women and five clinical experts were conducted in Qingdao between March and May 2024. The experts engaged in clinical practice and management of obstetrics and gynecology, three of the five experts specialized in maternal and child health and one in psychology. Criteria for participation in the study were pregnancy, age 18 years or older, smartphone ownership, healthy cognitive function, and verbal communication. Exclusion criteria were mental illness, cognitive, severe pregnancy complications, and serious medical conditions such as severe heart failure and hypertension. The following questions were posed to the experts: (I) What e-health literacy do women need during pregnancy? Please illustrate with an example; (II) What are the differences in e-health literacy for pregnant women compared to that for other groups? (III) What aspects of e-health literacy should be evaluated during pregnancy? (IV) How should the level of e-health literacy of women during pregnancy be improved? The following questions were posed to the pregnant women: (I) What channels do you generally use to obtain health information during pregnancy? (II) How well do you understand the available e-health information? (III) How do you distinguish inconsistent health information on pregnancy on the internet? (IV) Will you comment on the internet information when it is inconsistent with the pregnancy health information that you know? (V) How do you use e-health information to manage your own health? The Colaizzi seven-step analysis method was used to analyze the interview content (13): (I) repeatedly reading the transcribed content; (II) extracting the content related to e-health literacy during pregnancy; (III) coding the content with obvious significance and reappearance; (IV) extracting common characteristics as the initial theme; (V) describing the meaning of each initial theme; (VI) sorting again, finding the internal connection between the themes, and finally forming the theme group; (VII) returning the analyzed results to the interviewees for confirmation. Ultimately, we added four items: (I) I go online to provide the doctor with diagnostic information such as pregnancy symptoms and health indicators and to find and share pregnancy health information; (II) I pay attention to protect my privacy and that of others; (III) I will use pregnancy-related website related functions; (IV) I can quickly query the author’s credibility, identify the network, obtain the period of pregnancy, and determine whether health information is a rumor. Based on the results of the literature review and the qualitative analysis, we developed a preliminary e-health literacy scale for pregnant women encompassing four dimensions and 49 items.

Delphi expert consultation

A total of 23 experts were recruited into this study via convenience sampling to conduct a Delphi consultation. Expert inclusion criteria comprised: (I) holding at least a bachelor’s degree; (II) having a minimum of 10 years of experience in pregnancy diagnosis and treatment, clinical nursing, or nursing management; (III) being engaged in e-health literacy or pregnancy care-related fields; and (IV) voluntarily agreeing to participate. The consultation was carried out over two rounds using email-distributed questionnaires.

The expert authority coefficient, which reflects the reliability and expertise of the panel, was calculated based on each expert’s familiarity with the topic and the rationale underlying their judgments. A coefficient above 0.70 was considered acceptable (14). The Kendall harmony coefficient was used to evaluate the level of consensus among experts regarding scale content, with values closer to 1 indicating stronger agreement (15). Following the two Delphi rounds, a preliminary version of the e-health literacy scale for pregnant women was formed, consisting of 25 items across four dimensions.

Item analyses

First on-site investigation

Based on the principle that the sample size should be at least 5 to 10 times the number of items in the scale. We randomly selected 220 pregnant women from September to November 2024 at The Affiliated Hospital of Qingdao University as a convenience sample. The inclusion criteria were as follows: (I) pregnancy (16); (II) has a smart phone; (III) normal communication and understanding ability; and (IV) being informed of the purpose of the study and volunteering to participate. The exclusion criteria were as follows: (I) mental illness or a cognitive or consciousness disorder; (II) severe pregnancy complications; and (III) serious diseases such as severe heart failure and high blood pressure. Two scales were used to collect data for this study: (I) the general condition scale, including age, area, culture, degree, physical condition, and (II) the initial e-health literacy scale for pregnant women, using a 5-point Likert scale ranging from 1 (never) to 5 (always), with higher scores indicating higher levels of e-health literacy.

The entries were screened and tested for differentiation, discriminatory power, sensitivity and representativeness, and internal consistency the critical ratio method, correlation coefficient method, Cronbach’s alpha coefficient method and factor analysis, with the following criteria for entry deletion.

• Critical ratio method: the entry scores of the study participants were arranged in descending order, with the bottom 27% of the scores being the high subgroup and assigned a value of 1, and the top 27% of the scores being the low subgroup and assigned a value of 2. An independent samples t-test was performed on the scores of the two groups, and if the difference was not statistically significant or if the t-threshold value was less than 3, it should be eliminated (17).
• Correlative coefficient method: the correlation coefficient between the score of each entry and the total score of the scale was calculated, and if Pearson’s correlation coefficient between the entry and the scale was less than 0.4 or did not reach a significant level, it was considered to be excluded (18).
• Cronbach’s alpha coefficient method: calculate the total Cronbach’s alpha coefficient for the scale, if the Cronbach’s alpha coefficient for the total scale is significantly higher with the exclusion of an entry, exclude it (19).
• Factor analysis method: it is generally considered that the Kaiser-Meyer-Olkin (KMO) value >0.8, and the larger the KMO value, the more suitable for factor analysis; Bartlett’s test statistic is appropriate for factor analysis when it reaches a significant level. Factor loadings <0.4, two or more common factor loadings >0.4, and the absolute value of the difference in loadings <0.2 should be excluded (20).

Second on-site investigation

We selected 230 pregnant women from December 2024 to February 2025 from five hospitals (The Affiliated Hospital of Qingdao University, Qingdao Municipal Hospital, Qingdao Central Hospital, Shandong University Affiliated Hospital, Laixi People’s Hospital) as an additional convenience sample. Patients’ data were collected through an online questionnaire. The inclusion criteria for the patients were the same as those described above. Two scales were used to collect data for this study: (I) the general condition scale, with the content the same as described above; and (II) the e-health literacy scale for pregnant women, which used a 5-point Likert scale ranging from 1 (never) to 5 (always). Higher scores indicated higher levels of e-health literacy.

Reliability of the e-health literacy scale for pregnant women

Cronbach’s alpha coefficient is the most commonly used index for evaluating the internal consistency of scale dimensions and the overall internal consistency of the scale, with Cronbach’s alpha coefficients >0.8, indicating that the reliability of the scale is good (21). The entries of each dimension and all the entries of the scale were divided into two halves according to the parity of the serial number, and after deriving the correlation coefficient r of the scores of the entries of the two parts of the parity, the split-half reliability was calculated by adopting the formula R=2r/(1 + r), and it is generally believed that the split-half reliability coefficient should be >0.7 (19). Test-retest reliability is an assessment of scale stability and consistency over time. We evaluated the test-retest reliability with 30 pregnant women 2 weeks after the first survey. It is usually required that the test-retest reliability should be ≥0.7 (22).

Validity of the e-health literacy scale for pregnant women

• Content validity: two obstetric clinicians, four obstetric clinical nurses, and one nurse educator were selected to rate the importance of each entry on the scale, and content validity indices were calculated at the entry level [item-level content validity index (I-CVI)] and the scale level [scale-level content validity index (S-CVI)]. An acceptable I-CVI of items was >0.75, and an acceptable S-CVI of items was >0.9 (23).
• Construct validity: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the construct validity of the scale. Half of the sample was randomly selected for EFA. Dimensions were delineated through screening common factors, and the extracted factorial structure was then compared to the previously established constructs. CFA was conducted on the remaining sample and goodness-of-fit indices were used to verify the consistency of the theoretical structure obtained by EFA.
• Convergent and discriminant validity: convergent validity was assessed using the average variance extracted (AVE) and composite reliability (CR). An AVE >0.5 and CR >0.6 mean good convergent validity (24). The AVE square root of the factor, and the correlation coefficients between the factor and other factors, were used to compare the discriminant validity; an AVE square root > the correlation coefficient of the factors indicated good discriminant validity (25).

Statistical analysis

Statistical analysis was performed using Excel, AMOS 24.0, and SPSS 26.0. Count data were described using case numbers and percentages; measurement data that met normal distribution requirements were represented by mean ± standard deviation. P<0.05 was considered statistically significant.

Results

Delphi expert consultation

Twenty-three experts were selected from 10 tertiary hospitals and medical universities across nine provinces and municipalities. Twenty-six expert questionnaires were distributed in the first round and 23 in the second round, with a valid response rate of 88% and 100%. The positive coefficients of the two rounds of expert consultations were expressed by the effective recovery rate, which was 93.33% and 100%. In this study, the coefficient of expert authority in the first round was 0.891, and the coefficient of expert authority in the second round was 0.897, both of which were ≥0.700, indicating a high degree of expert authority. The Kendall harmonic coefficients were 0.386 in the first round and 0.439 in the second round. Based on the criteria for selection of entries and detailed comments from experts, the team discussed and revised the scale. Twelve items were revised, 17 items were excluded, and 15 items were combined. In the second round, all the items were retained (the comments on item modification are shown in Table S1). The results of the expert consultation are shown in Table 1. The initial eHEALS included four dimensions and 25 items (Table 2). The language used in the scale development was Chinese.

Item analysis results

A total of 220 questionnaires were distributed and 216 were recovered, with an effective recovery rate of 98.18%. Participant ages ranged from 23–40 (30.6±3.84) years, 73 (33.8%) of the pregnant women had Bachelor’s degrees, 91 (43.13%) were urban residents, 122 (56.78%) were first pregnancies, 120 (55.56%) had frequent online medical experience; 149 (68.98%) would not study at a school for pregnant women, and 111 (51.39%) frequently retrieved health information from the web.

• Critical ratio method: the critical ratio t-values showed that all the items were significant (P<0.001) (Table 3).
• Correlative coefficient method: although the correlation coefficient of item D1 was 0.347, the significance level was P<0.001, so it was retained (Table 3).
• Cronbach’s alpha coefficient method: the Cronbach’s alpha coefficient for the total scale was 0.935, and the Cronbach’s alpha coefficient increased to 0.937 after the deletion of entry D1, and there was no increase in the Cronbach’s alpha coefficient after the deletion of the other entries, so entry D1 was excluded (Table 3).
• Factor analysis method: four common factors with eigenvalues greater than 1 were extracted, with a cumulative variance contribution rate of 63.434%. The factor loading values showed that items C1 and D2 with multiple loads (Table 4). Therefore, it is considered to delete items C1 and D2.

Reliability and validity results

A total of 230 questionnaires were distributed and 224 were recovered, with an effective recovery rate of 97.39%. The general information of participants are presented in Table 5. Internal consistency reliability: the alpha value for the scale overall was 0.937, and the Cronbach’s alpha coefficients for the dimensions ranged from 0.838 to 0.927. The split-half reliability value for the scale overall was 0.881 and for each of the dimensions ranged from 0.797 to 0.855 (Table 6). Stability: after two weeks, the test-retest reliability of the overall scale was 0.772, and that of each construct was 0.769–0.821; both were significant (P<0.01) (Table 6). Content validity: the S-CVI of the overall scale was 0.962, and I-CVI of each item was 0.869–1.000. Construct validity: the KMO value was 0.878 and Bartlett’s test spherical yielded χ²=3,488.236 (P<0.001), indicating the sample was suitable for factor analysis. The results of CFA fit indices indicate: [χ²/df =2.250, root mean squared error of approximation (RMSEA) =0.075, Comparative Fit Index (CFI) =0.930, Goodness-of-Fit Index (GFI) =0.849, Incremental Fit Index (IFI) =0.931, Tucker-Lewis Index (TLI) =0.919, Normed Fit Index (NFI) =0.882], the CFA model fits well. Four common factors with feature values greater than 1 and cumulative variance contribution rate was 64.159%. The factor loading and commonality of each item were greater than 0.4 (Table 7). The ability to obtain e-health information contained 5 items, with factor loading values ranging from 0.518 to 0.777. The ability to assess e-health information contained 7 items; the factor loading values ranged from 0.649 to 0.884. The ability to interact with e-health information contained 4 items; the factor loading values ranged from 0.678 to 0.788. The ability to apply e-health information contained 6 items; the factor loading values ranged from 0.401 to 0.773.

Convergent and discriminant validity: the factor loadings corresponding to each item across the four dimensions were all >0.500. The AVE for each dimension were all above 0.500, and the CRs were all >0.700. Furthermore, discriminant validity of the scales showed that there were significant correlations between the four dimensions, with the correlation coefficients being smaller than the corresponding square root of the AVE (Tables 8,9).

The missing data rate was very low (0.3% for the entire dataset), with no single item having more than 1.5% missing values. Missing data were handled using mean substitution for the affected items.

Discussion

The internet has emerged as a pivotal channel for health information, a trend particularly evident among pregnant women (26,27). Contrary to the assumption that stable health and community support might reduce information-seeking, research indicates that pregnant women actively seek online information to supplement advice from healthcare providers and family, to understand bodily changes, and to prepare for parenthood, often driven by a desire for timely, diverse, and peer-based knowledge (28,29). Studies confirm that the internet serves as a primary information source for this demographic (30,31). However, the vast and unregulated nature of online health information (32) poses significant challenges in assessing its reliability. These challenges can provoke anxiety, stress, and fear, which may adversely affect pregnancy outcomes (33). Existing universal e-health literacy scales, such as the unidimensional eHEALS, often focus on individual information retrieval skills and lack comprehensive integration of the social and interactive dimensions critical to modern digital health engagement, including the use of social media and online communities (34).

The present study developed and validated a targeted e-health literacy scale for pregnant women based on the TMeHL by Paige (12). This framework’s four interdependent dimensions provided a structured, bottom-up cognitive process for scale construction. The initial item pool was rigorously developed through a literature review, semi-structured interviews, and a two-round Delphi expert consultation. The 23 experts involved, all with over 20 years of experience and expertise in e-health management, ensured the process’s representativeness and authority. The final scale comprised 25 items across four dimensions: acquisition, evaluation, interaction, and application abilities. Item analysis led to the removal of three items, resulting in a 22-item formal scale. The entire development process was scientific and standardized, ensuring the content validity and relevance of the items.

The psychometric properties of the scale were robust. The high internal consistency (Cronbach’s α=0.937, split-half reliability =0.881) and good test-retest reliability (r=0.772) confirm its reliability. Validity was also strongly supported. The scale-level and item-level content validity indices exceeded recommended thresholds. Exploratory and confirmatory factor analyses validated the hypothesized four-factor structure, which explained 64.2% of the total variance. The model fit indices from CFA were acceptable, and the scale demonstrated good convergent and discriminant validity, as evidenced by satisfactory AVE, CR values, and inter-dimension correlations.

A key contribution of this study is the development of a multi-dimensional scale specifically for pregnant women, addressing a gap in the existing toolkit. When compared to the widely used eHEALS, which is unidimensional and focuses on perceived skills for locating and evaluating health information, our scale delineates distinct competencies in acquisition, evaluation, interaction, and application. This structure aligns more closely with the multi-dimensional eHealth Literacy Questionnaire (eHLQ) but is uniquely tailored to the pregnancy context, particularly in its inclusion of the “interaction ability” dimension. This dimension captures behaviors like sharing information with peers and protecting privacy online, which are salient for pregnant women engaging in social media and online forums but are not explicitly measured by general scales (35). Our findings that evaluation ability had the highest internal consistency resonate with studies using general scales in pregnant samples, which also highlight the critical need for discernment in navigating online information (36). The reliability and validity coefficients observed in our study are comparable to, and in some cases superior to, those reported for other e-health literacy measures, supporting the psychometric rigor of this new tool.

The practical implications of this scale are substantial. It enables a targeted assessment of e-health literacy in pregnant women, moving beyond the abstractness and potential insensitivity of general scales (37). The items comprehensively cover not only the core skills of finding, judging, and using online information but also extend to interacting with online services and communities. This allows healthcare providers to identify specific literacy deficits—for instance, a low score in “evaluation ability” might indicate a need for guidance on critiquing source credibility, while a low “interaction ability” score could suggest hesitancy in online peer support.

While this study was motivated by the need to capture the social and interactive dimensions of e-health literacy, as highlighted by the TMeHL framework, the final scale’s ‘Interaction ability’ dimension may not fully encompass the entire spectrum of social media behaviors. Although items related to sharing information with peers (C2) and protecting privacy during online interactions were included, specific behaviors such as following health influencers, evaluating user-generated content against professional advice, or navigating conflicting advice within online communities were not explicitly captured as standalone items (38). This may be partly due to the Delphi experts’ prioritization of more foundational and broadly applicable skills. Future iterations of the scale could benefit from explicitly generating and testing items that target these nuanced social media activities, thereby strengthening its capacity to measure the full scope of digital health engagement in the modern information landscape (39).

Several limitations of this study should be acknowledged. First, the scale was developed using a restricted sample drawn solely from China through non-probability sampling, which may affect the generalizability of the findings. Future research should implement multi-center designs with larger and more diverse samples. Furthermore, the scale’s applicability requires additional refinement and validation across culturally varied populations.

Conclusions

An e-health literacy scale for pregnant women was developed in this study. The scale consists of four dimensions: the ability to obtain, assess, interact with, and apply e-health information. It includes a total of 22 items. Scores were obtained using a 5-point Likert scale. The e-health literacy scale showed satisfactory psychometric properties within three reliability indicators (Cronbach’s alpha coefficient, split-half reliability, and test-retest reliability) and four validity indicators (content, construct, convergence, and discrimination validity). Additionally, the scale has strong practicability and feasibility for pregnant women, and can be used as a tool to evaluate women’s e-health literacy during pregnancy, formulate feasible e-health education plans, and improve self-management in pregnancy.

Acknowledgments

The authors would like to acknowledge all the experts in Delphi consultation and the pregnant women in this study for their contributions.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-48/rc

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-48/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-48/prf

Funding: This work was supported by the Shandong Provincial Health Commission (grant No. 202414030736), Chinese Nursing Association (grant No. ZHKY202423), and Affiliated Hospital of Qingdao University (grant No. QDFYQN2024119).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-48/coif). All authors report that this study was supported by the Shandong Provincial Health Commission (grant No. 202414030736), Chinese Nursing Association (grant No. ZHKY202423), and Affiliated Hospital of Qingdao University (grant No. QDFYQN2024119). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of Qingdao University (No. QDU-HEC-20221 89) and informed consent was obtained from all individual participants. All participating hospitals were informed of and agreed on the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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