GRAPHICAL ABSTRACT

INTRODUCTION

Health literacy (HL) is a key determinant of population health and is closely linked to social gradients in health outcomes. Defined as the cognitive and social skills required to access, understand, appraise, and apply health information for care, prevention, and health promotion [1–3], limited HL can hinder treatment adherence, prescription comprehension, and navigation of health systems, thereby contributing to poorer disease control and increased morbidity and mortality [4–6]. Accordingly, HL is widely recognized as a central pillar of global health promotion and health system-strengthening frameworks [7].

The concept of HL has expanded from basic reading skills to a multidimensional construct encompassing functional, communicative, critical, and digital competencies [3,8,9]. Communicative literacy enables effective interaction with health professionals, whereas critical literacy supports the appraisal and application of reliable information in everyday health decisions [8]. Digital HL—the ability to access, evaluate, and use online health information—has gained increasing importance with the expansion of telehealth and mobile health platforms [2,10,11]. These competencies are strongly shaped by education, age, and access to technology, with digital skills increasingly interacting with traditional HL to influence preventive behaviors and health outcomes [10,12,13]. Collectively, HL reflects both individual capacity and broader structural determinants, including education, income, and access to information [8–10].

In low-income and middle-income countries, including India, limited HL remains a major barrier to equitable healthcare utilization [13,14]. Surveys have reported widespread difficulties in understanding health information and navigating health systems, particularly among rural populations with lower educational attainment [6,13,14]. These gaps are associated with poor chronic disease management and increased vulnerability to health misinformation, particularly during the coronavirus disease 2019 (COVID-19) pandemic [6,15]. Accordingly, there is a clear need for reliable, culturally adapted, and linguistically validated HL assessment tools in Indian languages [16–18].

Globally, validated instruments assess distinct domains of HL. The Short Assessment of Health Literacy in English (SAHL-E) measures word recognition and comprehension [19]; the All Aspects of Health Literacy Scale (AAHLS) evaluates functional, communicative, and critical literacy [8]; and the European Health Literacy Survey Questionnaire (HLS-EU-Q47) assesses access, understanding, appraisal, and application of health information across healthcare, disease prevention, and health promotion contexts [3,20]. These tools demonstrate acceptable to strong psychometric performance across diverse populations and languages [10,21,22], forming a comprehensive framework for HL assessment [3,9]. However, systematic adaptation for Hindi-speaking populations remains limited [14].

Existing Indian translations of HL instruments often lack expert review, pretesting, or psychometric validation, thereby limiting their reliability and comparability [14]. Rigorous cross-cultural adaptation must preserve conceptual and semantic equivalence in order to minimize measurement bias [21,23–26]. This process is particularly challenging in India because of its linguistic diversity and the widespread barriers to adults’ comprehension of health information [14].

Hindi, spoken by more than 600 million people across northern and central India, is widely used in health communication [27]. However, the lack of validated Hindi HL instruments represents a major gap in research and policy. Standardized tools are essential for effective health communication design, program evaluation, and evidence-based planning. Accordingly, this study aimed to translate, culturally adapt, and psychometrically validate 3 widely used instruments (SAHL-E, AAHLS, and HLS-EU-Q47) in Hindi among rural adults in North India, following internationally accepted adaptation and validation guidelines [13,23,25].

METHODS

Study Design and Setting

A community-based cross-sectional psychometric validation study was conducted between January 2020 and June 2020 in the Rural Health Training Centre (RHTC) field practice area of Muzaffarnagar Medical College, Muzaffarnagar, India. The study aimed to translate, culturally adapt, and validate 3 HL instruments (SAHL-E, AAHLS, and HLS-EU-Q47) for Hindi-speaking adults. The RHTC serves 5 villages with approximately 32 000 residents, most of whom live in agrarian or semi-skilled households typical of rural North India. Health services in the area are supported by sub-centers, primary health centers, and community outreach workers, who facilitate community-based validation.

Sampling Strategy and Participants

A multistage stratified random-sampling design was used. Five villages were selected using probability proportional to size sampling, followed by systematic household sampling. Within each household, 1 Hindi-speaking adult (≥18 years) was selected using the last-birthday method. Eligible participants were permanent residents who were fluent in Hindi and provided written informed consent. Individuals with major sensory or cognitive impairment were excluded. Sample size was determined using psychometric recommendations of 5 to 10 participants per item for exploratory factor analysis [13,23,28]. For the 47-item HLS-EU-Q47 instrument, this approach suggested a target sample of approximately 235–470 participants. A feasible sample of 250 participants was therefore enrolled and was considered adequate for stable factor extraction and initial cross-cultural validation [23,29]. For test–retest reliability assessment, 30 participants were re-interviewed after 2 weeks, consistent with standard psychometric practice [28,30]. Similar retest sample sizes and intervals have been reported in previous Asian HL validation studies [20,24,31].

Study Instruments

Three established instruments were administered. The SAHL-E (18 items) assesses functional HL through word recognition and comprehension [19]. The AAHLS (14 items) measures functional, communicative, and critical HL [8]. The HLS-EU-Q47 (47 items) evaluates access to, understanding of, appraisal of, and application of health information across healthcare, disease prevention, and health promotion domains [3,20]. Scoring followed the developers’ guidelines, with higher scores indicating greater HL. Permissions were obtained where required. Possible score ranges were 0–18 for SAHL-E, 14–70 for AAHLS, and 47–188 for HLS-EU-Q47.

Translation and Cultural Adaptation

Cross-cultural adaptation followed internationally accepted guidelines [16,18,21,32]. Two bilingual translators independently produced forward translations, which were reconciled by a multidisciplinary expert panel. Blinded back-translations were subsequently performed and assessed for semantic, idiomatic, experiential, and conceptual equivalence. Discrepancies were resolved by consensus. Pretesting was conducted with 20 adults to evaluate clarity and cultural acceptability. Cognitive interviews led to minor wording simplifications (e.g., replacement of technical symptom-management terms). The final Hindi versions were then approved and proofread for linguistic accuracy.

Data Collection

Trained Hindi-speaking field staff conducted home-based interviews using a standardized protocol. Field staff completed a two-day training program covering interviewing techniques, instrument administration and scoring, research ethics, and confidentiality procedures. Instruments were administered in randomized order to minimize order effects. Interviews lasted approximately 30–35 minutes. Quality-control procedures included random spot checks and daily schedule reviews. Data were double-entered in Microsoft Excel (Microsoft Corp., Redmond, WA, USA) and cross-verified for accuracy.

Variables and Data Management

Socio-demographic variables included age, sex, marital status, education, occupation, income, and village of residence. Education was categorized according to the highest level of attainment, and occupation was classified using the National Classification of Occupations 2020. SAHL-E, AAHLS, and HLS-EU-Q47 scores were analyzed as continuous variables. Data underwent range and logic checks. Minimal missing data (<2%) were handled using domain-wise score averaging, a method commonly applied in psychometric validation studies [28]. All data were password-protected and accessible only to the investigator.

Statistical Analysis

All analyses were conducted using SPSS version 26 (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to summarize socio-demographic characteristics and HL scores (Tables 1 and 2). Internal consistency was evaluated using the Cronbach α (acceptable ≥0.70). Test–retest reliability was assessed using a two-way mixed-effects intraclass correlation coefficient (ICC [3,1]). ICC values were interpreted as poor (<0.50), moderate (0.50–0.74), good (0.75–0.89), and excellent (≥0.90) [28]. Construct validity was assessed using exploratory factor analysis with principal-axis factoring and Promax rotation after confirming sampling adequacy using the Kaiser–Meyer–Olkin statistic and the Bartlett test of sphericity [28,29], as the factor structure could not be assumed a priori. Convergent validity was assessed using Pearson correlation coefficients among total scores. Known-groups validity was examined using analysis of variance with Bonferroni post hoc comparisons across education and occupation categories. Feasibility indicators (completion rate, missing responses, interview duration, and participant feedback) were summarized descriptively. Scree plots, correlation heatmaps, and boxplots were used to illustrate construct validity, convergent validity, and known-groups validity. Statistical significance was defined as p<0.05 (two-tailed).

Ethics Statement

Ethical approval was obtained from the Institutional Ethics Committee of Muzaffarnagar Medical College, Muzaffarnagar, India. Written informed consent was obtained from all participants after an explanation of the study objectives and procedures. Participants were assured of confidentiality and their right to withdraw from the study at any time without consequences. All data were anonymized and securely stored. The study adhered to the ethical principles of the Declaration of Helsinki (2013 revision) and the Indian Council of Medical Research National Ethical Guidelines (2017).

RESULTS

A total of 250 adults participated in the study conducted in the RHTC field Muzaffarnagar Medical College, Muzaffarnagar, Uttar Pradesh, India. The mean age was 42.1±12.3 years (range, 18–70); 48.8% of participants were male and 51.2% were female. Most participants were married (68.0%), followed by those who were never married (18.0%) and those who were widowed or divorced (14.0%). Educational attainment ranged from no formal schooling (19.6%) to graduate education (8.0%). Major occupational groups included farming (25.2%), wage labor (22.0%), homemaking (24.8%), shopkeeper/self-employed (10.0%), and student (6.0%). The median monthly household income was Indian rupee 14 800 (range, 1000–80 000). All 5 villages were represented in accordance with the sampling framework (Table 1).

Descriptive Analysis

Mean total scores were 11.9±4.1 for SAHL-E, 49.6±8.5 for AAHLS, and 138.2±17.6 for HLS-EU-Q47 (Table 2). Score distributions were approximately normal (skewness <1), with minimal floor and ceiling effects. Within the HLS-EU-Q47, scores were higher for the accessing and understanding domains than for the appraising and applying domains.

Internal Consistency

Cronbach α values indicated high internal consistency for all instruments: SAHL-E (α=0.84), AAHLS (α=0.87; subscale α=0.79–0.82), and HLS-EU-Q47 (α=0.93; domain-specific α≥0.85), all exceeding accepted thresholds for group-level reliability (Table 3) [28]. Inter-item and item–total correlations ranged from 0.32 to 0.71, indicating adequate internal homogeneity. Detailed correlation matrices are presented in Supplemental Material 1.

Test–retest Reliability

Thirty participants were re-interviewed after 14±2 days. Test–retest reliability, assessed using a two-way mixed-effects ICC [3,1], demonstrated strong temporal stability for SAHL-E (0.88), AAHLS (0.91), and HLS-EU-Q47 (0.94), with all subscales exceeding 0.80. Overall ICC values (0.86–0.94) met the criteria for good-to-excellent reliability (Table 4) [28].

Construct Validity

Exploratory factor analysis (principal-axis factoring with Promax rotation) supported the construct validity of all 3 instruments. Sampling adequacy was strong (Kaiser–Meyer–Olkin=0.82), and the Bartlett test of sphericity was significant (χ²=1245.7, p<0.001). SAHL-E showed a single-factor solution (eigenvalue=4.62; 25.7% variance explained). AAHLS yielded a 3-factor structure explaining 33.8% of the variance, with factor loadings ranging from 0.42 to 0.80. HLS-EU-Q47 produced a 4-factor structure explaining 32.1% of the variance, with loadings ranging from 0.44 to 0.84. Detailed factor loadings are presented in Supplemental Material 2, and scree plots are shown in Figure 1.

Convergent Validity

Total scores were positively correlated: SAHL-E with AAHLS (r=0.48), SAHL-E with HLS-EU-Q47 (r=0.42), and AAHLS with HLS-EU-Q47 (r=0.61); all correlations were significant at p<0.001. Correlation matrices are presented in Supplemental Material 1, and the correlation patterns are illustrated in Supplemental Material 3.

Known-groups Validity

Scores increased with educational attainment and occupational skill level. Participants with no formal schooling had the lowest scores, whereas those with graduate education had the highest. Differences according to education were significant (p<0.001), and Bonferroni-adjusted post hoc tests confirmed differences between successive educational levels. Occupational differences were also significant (p=0.008), with higher scores observed among participants in skilled occupations or student groups than among those engaged in farming or wage labor. No significant differences were observed according to sex or marital status. Detailed subgroup comparisons are presented in Supplemental Material 4, and score distributions according to education level are illustrated in Supplemental Material 5.

Feasibility and Acceptability

All instruments demonstrated good feasibility. Mean administration times were 9.6 minutes for SAHL-E, 7.8 minutes for AAHLS, and 14.4 minutes for HLS-EU-Q47, with a mean overall interview duration of approximately 32.0 minutes. Completion rates exceeded 98%, and missing responses were <2%. Detailed feasibility indicators are summarized in Supplemental Material 6. Participants reported high clarity (mean comprehension rating, 4.2/5.0), although mild fatigue was reported by 12.8% of participants during HLS-EU-Q47 administration. No interviews were discontinued. These findings are consistent with international recommendations for cross-cultural instrument adaptation and field testing [16,18,24].

DISCUSSION

Overall Psychometric Performance and Validation Summary

All 3 Hindi-adapted instruments demonstrated acceptable reliability, validity, and feasibility in rural North India. Reliability exceeded conventional benchmarks (Cronbach α ≥0.84; ICC ≥0.86), and moderate-to-strong inter-instrument correlations supported convergent validity. SAHL-E provided an objective assessment of functional HL, AAHLS captured self-reported functional and critical literacy, and HLS-EU-Q47 enabled multidomain evaluation of health-related information processing. Following internationally accepted guidelines [16,18,24,32], the Hindi versions demonstrated strong internal consistency, temporal stability, construct validity, and feasibility. These findings indicate conceptual equivalence with the original instruments, are consistent with multidimensional HL models, and support the value of these tools for strengthening HL measurement in this setting [2,9,11,33].

Socio-demographic Context and Descriptive Distribution

The sample reflected the educational and occupational diversity of rural western Uttar Pradesh and showed sufficient score variability for psychometric testing (Tables 1 and 2). Consistent with evidence from rural India and other low-income and middle-income settings, lower educational attainment was associated with poorer comprehension and greater difficulty navigating health systems [14,31,34]. This educational gradient highlights the influence of social determinants—particularly education, occupation, and access to information —on HL in rural settings [8,10,27]. It also underscores the value of culturally adapted tools for identifying disparities and informing targeted community-based and primary care interventions.

Reliability and Internal Consistency

All 3 instruments demonstrated high internal consistency (Cronbach α=0.84–0.93), exceeding the accepted threshold of ≥0.70 for group-level comparisons [28,35]. HLS-EU-Q47 showed the strongest reliability, consistent with its multidomain structure and with previous European and Asian validation studies [20,36]. AAHLS retained its original subscale structure [8], and SAHL-E showed reliability comparable to that reported for Spanish and English adaptations [19]. Temporal stability was also strong (ICC=0.86–0.94), meeting accepted reproducibility criteria [28] and supporting the use of the Hindi adaptations in both cross-sectional and repeated community-based assessments.

Construct and Structural Validity

Exploratory factor analysis supported the expected theoretical structures of all 3 instruments. SAHL-E was unidimensional, AAHLS showed a 3-factor structure (functional, communicative, and critical), and HLS-EU-Q47 retained a 4-factor structure (accessing, understanding, appraising, and applying). Sampling adequacy was acceptable (Kaiser–Meyer–Olkin=0.82), and the significant Bartlett test further supported construct validity [37]. These findings are consistent with previous European and Asian validation studies [1,10] and suggest that rigorous translation and cross-cultural adaptation can preserve the conceptual and structural integrity of HL constructs across sociocultural contexts [16,18,21,23–25].

Convergent and Known-groups Validity

Moderate-to-strong inter-instrument correlations (r=0.42–0.61) supported convergent validity, indicating that the instruments assess related but distinct dimensions of HL. The stronger correlation between AAHLS and HLS-EU-Q47 likely reflects overlap in functional and critical literacy domains [8,38], whereas the weaker correlations involving SAHL-E highlight the complementary roles of objective and self-reported measures [10,29]. Clear gradients according to education and occupation further supported known-groups validity, consistent with findings from Myanmar, Vietnam, and rural India [14,30,31]. Together, these results support the sensitivity of the Hindi-adapted instruments to expected subgroup differences and their suitability for population-level and policy-relevant assessment.

Feasibility and Acceptability

All 3 instruments showed high feasibility and acceptability, with completion rates >98%, brief administration time (approximately 32 minutes overall), and good participant comprehension. Field staff reported minimal difficulty with administration, although mild fatigue was noted with the longer HLS-EU-Q47. These findings are consistent with previous Asian applications of self-reported HL instruments, including eHealth Literacy Scale (eHEALS)-based studies [10,22], and support the integration of the Hindi-adapted tools into large-scale Indian surveys and program evaluations.

Comparison with International Evidence

The psychometric properties of the Hindi-adapted instruments were comparable to those reported in international validation studies. The internal consistency of HLS-EU-Q47 and AAHLS aligned with that reported in previous cross-cultural adaptations in European, Asian, and Latin American populations [20,36,39], whereas the reliability of SAHL-E was similar to that of the original English and Spanish versions [19]. Educational gradients and inter-instrument correlations were also consistent with findings from European surveys and studies conducted in low-income and middle-income settings [1,30,31]. Together, these comparisons support the cross-cultural transferability of HL constructs and indicate that systematic translation, expert review, and cognitive testing can yield psychometrically comparable instruments across diverse contexts [16,18,21,23–25].

Methodological Strengths

This study followed established best practices for scale translation and psychometric validation [21,23–25,28]. Multistage probability sampling across 5 villages enhanced representativeness and minimized selection bias. Independent forward translations and blinded back-translations, combined with expert review, helped ensure semantic and conceptual equivalence [21,23–25]. In addition, cognitive pretesting and standardized interviewer training reduced the risk of cultural and interviewer-related bias. Statistical analyses followed accepted psychometric standards for factor extraction, reliability, and validity assessment [28,29,35,37], thereby supporting analytical rigor and reproducibility.

Interpretation and Policy Implications

The Hindi-adapted SAHL-E, AAHLS, and HLS-EU-Q47 capture complementary dimensions of HL: SAHL-E as a rapid objective screener, AAHLS as a concise self-reported instrument for community surveys, and HLS-EU-Q47 as a comprehensive multidomain tool for baseline assessment and program evaluation. Integrating these tools into India’s health system aligns with national priorities and global frameworks related to equity and empowerment and responds to calls for rigorous, theory-informed measurement [7,27,33]. Embedding brief assessments within initiatives such as Ayushman Bharat or the National Health Mission may help identify populations with limited HL and guide targeted support. As digital health continues to expand, the inclusion of digital HL components will become increasingly important for assessing the ability to access, appraise, and use online health information [2,11,12,18]. This broader integration could strengthen health communication and improve health system responsiveness.

Limitations and Future Directions

These findings are primarily generalizable to rural Hindi-speaking adults, and replication in urban populations and other linguistic groups is needed [40]. Interviewer-administered assessments may have introduced social desirability bias, although standardized procedures and confidentiality safeguards likely minimized this risk. Item simplification during pretesting suggests that minor further refinement may still be warranted. Criterion validity—specifically, the relationship of HL scores to outcomes such as treatment adherence, healthcare utilization, and disease control—requires further study, consistent with evidence linking limited HL to poorer chronic disease outcomes [4–6]. Future research should apply confirmatory factor analysis and measurement-invariance testing, incorporate digital HL tools such as eHEALS to capture online information use [11,15], and extend these instruments to additional Indian languages using comparable protocols for national benchmarking.

Data Availability

Supplemental Materials

Notes

Conflict of Interest

The author has no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

The author thanks the field staff of the Rural Health Training Centre, Muzaffarnagar Medical College, for their assistance with data collection and coordination with community participants. The author also thanks the participants for their cooperation.

Author Contributions

All work was done by Sharma D.

Figure 1

Scree plots and parallel analysis of Hindi-adapted health literacy instruments (A) SAHL-E, (B) AAHLS, and (C) HLS-EU-Q47. Scree plots and parallel analysis results from exploratory factor analysis of the Hindi-adapted health literacy instruments. SAHL-E, Short Assessment of Health Literacy in English; AAHLS, All Aspects of Health Literacy Scale; HLS-EU-Q47, European Health Literacy Survey Questionnaire.

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