Introduction

Health literacy can be defined as the extent to which members of the population adequately have access to health-related information, possess the ability and skills to comprehend and assimilate it to avail healthcare services, and make critical decisions regarding their own health and that of their family members1,2,3,4,5,6. Health literacy is a multidimensional7 construct that depends on the skills, knowledge, and beliefs of the individual in understanding and applying health-related information to manage their health and navigate the health care system and administration. This multidimensional aspect of health literacy goes beyond fundamental reading, writing, and numerical comprehension to deal with health-related information5,7.

From a public health perspective, health literacy is highly vital for a country’s development as insufficient health literacy is reported to be correlated with risky health behaviours8poor utilization of preventive medicine services8,9,10inadequate understanding of the underlying disease conditions10increased healthcare expenditure10,11,12poor compliance with prescription medications13and increased mortality10,14. The ensuing disparities in health literacy among various demographic classes in the country would ultimately lead to discernible differences in public health and inequality9,15.

More importantly, inadequate health literacy is associated with the presence of chronic health conditions16,17,18,19 and worse mental health20,21. In a longitudinal study20 of subjects with drug addiction, low health literacy was associated with increased manifestation of depressive symptoms. In a recent cross-sectional nationwide study21 in Iran, poor health literacy was associated with worse mental health, and improving health literacy levels could reduce the psychological burden of the subjects. Similarly, low levels of health literacy are associated with the presence of multimorbid chronic conditions17 and worse outcomes of chronic conditions among people with diabetes19respiratory16and cardiovascular conditions18. Possible mechanisms mediating the relationship between health literacy and health include inadequate utilization of health care services, reduced adherence to prescribed medications, low levels of reading comprehension, and associated shame1,20.

College students represent a unique cohort for health literacy policy interventions for two reasons: they are prone to myriad mental health problems22,23,24,25and intervention at an early age would likely exert long-lasting impacts on their health. Additionally, the presence of university settings makes it more practical to devise health policy interventions as courses and modules and integrate them into the curriculum. Numerous studies have corroborated the fact that college students and members of the youth worldwide often go through periods of intense mental stress22,23,24,25, and discomfort, which could be a risk factor for developing numerous health-related ailments26,27,28,29. The solid psychological distress faced by college students could be attributed to several issues that include the following: increased academic duties and pressure, financial matters about repayment of college debt, being away from home, difficulties adjusting to a new environment, and personal issues23,30. The aforementioned issues among college students could lead to unhealthy health behaviours31,32,33,34lack of sleep and disrupted sleep patterns35and poor nutrition36. Universities and college campuses often administer wellness services such as peer support programs and professional support services to enhance student’s health and help navigate them through the complex academic environment37,38. In addition to the above-mentioned measures, students are entitled to and empowered to manage the various factors that control their own health39. One way to enable students to exert complete control over their health is by enhancing their health literacy40,41.

Numerous studies have looked into the distribution of health literacy profiles of college students and other factors that influence health literacy39. According to the results of these studies, health literacy is shaped by a host of sociodemographic factors of the student population39,42,43. A bulk of these studies were conducted in developed countries like the United States43Singapore44Germany45Australia46Denmark42and Taiwan47and only a handful of health literacy studies were conducted in developing2,48,49 nations. Although these studies help understand health literacy among college students worldwide, they don’t consider the factors specific to India, such as unique cultural factors and systemic barriers to health literacy50,51,52. The country’s diverse socioeconomic conditions and a large young population50,53 also warrant localised studies and subsequent comparison with global literature.

India faces unique challenges and opportunities on the health literacy front. India’s dual disease burden, characterized by a high prevalence of infectious and non-communicable diseases54,55 requires a diligent use of the country’s complex and rapidly developing health infrastructure50,51. The country’s population, especially college students, needs to understand the intricacies of using preventive and reactive health care practices and navigate a health care system that varies widely depending on the geographical location50,51 and separated linguistically, by culture, and by belief systems52. However, India also presents unique opportunities to enhance the health literacy of its residents, owing to digital health potential and high smartphone penetration56. Additionally, the presence of a large young population53 could help administer health literacy interventions (through schools and colleges) early in life, thereby making a long-lasting impact.

In India, systematic, comprehensive, and large-scale health literacy studies across the population are hard to find. A few studies are worth mentioning: the first one57 compared the health literacy of health sciences vs. non-health sciences subjects at a university using the Health Literacy Questionnaire (HLQ). The study found that health sciences subjects exhibited higher health literacy than non-health sciences subjects. Additionally, the study57 identified various obstacles to achieving good health literacy among its subjects. A similar study58, performed in a university setting using the HLQ-EU-Q1659, reported that the health literacy levels of the students vary depending on the courses enrolled and the year of study. Lastly, another study60, conducted in rural India, measured the health literacy of subjects and employed cluster analysis to identify distinct health literacy profiles in the study population. Numerous other health literacy studies were performed using unreliable or invalidated methods61,62,63,64. Additionally, some of these studies ignore the multidimensional aspect of the health literacy construct and don’t measure more than the reading, writing, and numeracy skills of the respondents61,65,66. Lastly, a significant chunk of the existing studies concerning health literacy measure the literacy pertaining to a specific domain (oral67 or mental68) and/or underlying disease conditions61,69 (diabetes, hypertension, etc.). Nevertheless, an overarching consensus among the results of existing studies is the absence of sufficient health literacy among the general public, which is also reinforced in a previous newspaper report70.

Therefore, a larger need exists to estimate health literacy among the Indian population and college students in particular, using a robust method that quantifies the intricate and complex nature of the health literacy construct. India’s unique position with a diverse population and healthcare problems presents a critical pivot to study and assess health literacy52,53. Enhancing health literacy levels in India could increase economic growth, reduce disease burden, and better prepare the world against the next pandemic7,10. Additionally, India’s health literacy blueprint could be helpful for other countries, especially those that are larger, face dual disease burden54,55 and health inequities50,51and aim to implement low-cost, digital, and community-based measures. In this study, we aim to elucidate the health literacy levels among college students using the multidimensional tool of the Health Literacy Questionnaire (HLQ)2,5,46. We hypothesize that certain sociodemographic and behavioural factors would influence the health literacy of college students in India and that these factors would affect the specific dimensions of the HLQ. Our study aims to decipher these associations and recommend health policies to promote health literacy among college students in India.

Results

Table 1 lists the sociodemographic characteristics of the subjects. All respondents are college students pursuing various majors in different years of study (first, second, third, and fourth/post-graduate diploma) at the university. Of the 200 respondents, 26% were males and 72% were females. Due to sample size limitations, the HLQ scores of the third-gender community were not considered for analysis in this study. As mentioned in the METHODS section, our study was designed to encourage participation from students in all years of study and all disciplines of study (purposive sampling71). Accordingly, the proportion of respondents who belong to a specific year of study and a particular discipline of study is given in Table 1. We also collected information about health insurance subscriptions, consumption of prescription medication, and information on the subject’s physical activity (usage of on-campus gym), family income, food preference, and presence of chronic illness among the respondents in the form of a self-reported supplementary questionnaire (Table 1). We chose to probe the relationship between family income and health literacy since several other studies have pointed out a more significant effect of income levels on health literacy43,72,73,74,75,76. This relationship deserves further exploration in a broader context since low family income is also known to be associated with poor health status and hospitalization16,18,19,72,76. Family income (annual) was categorized into three groups: Group 1 (< 90000 INR), Group 2 (between 90000 and 2 lakh INR), and Group 3 (> 2 lakh INR) according to the criteria mentioned in a previous report by the National Council of Applied Economic Research77. Next, food preference was also a predictor variable in the study. We probed this association because prior studies have established a connection between a vegetarian diet and better health outcomes78though methodological differences may preclude a broader interpretation. Additionally, food literacy and diet quality vary between people with different dietary lifestyles79. However, whether health literacy also differs considerably between people following various dietary regimens is currently not clearly elucidated. Lastly, for the presence or absence of chronic illness, the following conditions were considered: arthritis, back pain, asthma and lung problems, cancer, depression, anxiety, bipolar disorder, diabetes mellitus, stroke, thyroid problems, attention deficit hyperactivity disorder, and other self-declared illnesses.

Table 1 Sociodemographic information of the participants in the study.
Full size table

We next sought to understand the multiple dimensions of health literacy among the student population. As mentioned in the METHODS section, the HLQ scales5 can be broadly categorized into two categories: those with a maximum score of 4 (the first five scales) and those with 5 (the last four scales). This can be seen in Table 2, where scales HPS, HSI, AMH, SS, and CA belong to the former category, whereas the other scales AE, NHS, FHI, and UHI belong to the latter category. The HLQ has two types of scales to ensure that the scales adhere to their underlying constructs and simultaneously increase the accuracy of the responses while reducing bias5. While the 5-point scale adds increased granularity to the measured construct, the 4-point scale ensures that the responses are precise and decisive. The two different scales enable the HLQ to capture reliable responses that enhance its psychometric properties5. Among the scales with a possible scoring range from 1 to 4 (Scales 1 to 5), the lowest scores among the respondents were recorded for HSI (2.68 ± 0.47), and the highest was recorded for SS (3.17 ± 0.51) (Table 2). For the other category with a possible scoring range from 1 to 5 (scales 6 to 9), the lowest scores among the respondents were recorded for NHS (3.42 ± 0.66), and the highest was recorded for AE (3.59 ± 0.55) (Table 2). Note that the scores can’t be compared against each other but rather serve the purpose of apprising the readers of the various dimensions of health literacy.

Table 2 Nine scales of the HLQ and their descriptive statistics.
Full size table

We next proceeded with multivariate analysis of variance (MANOVA) to determine if any of the independent variables collected in the study had a significant effect on the different scales of health literacy of the respondents. Before the application of MANOVA, we carried out various statistical tests to ensure that the assumptions of the statistical procedure were adhered to in our sample. All dependent variables and their residuals were normally distributed (Additional file 2). In addition, a high correlation was observed between the nine dependent variables (Additional file 1), and the results of Levene’s test established equality of variance across various subject groups, with P-values ranging from 0.36 to 0.99. Upon application of MANOVA, we found the nine scales of the HLQ differed significantly based on the participant’s gender (Roy’s greatest root = 0.2069, p = 0), subscription to health insurance (Roy’s greatest root = 0.1432, p = 0.0024), usage of on-campus gym facilitates (Roy’s greatest root = 0.231, p = 0), and total family income (Roy’s greatest root = 0.1867, p = 0.0001). No statistically significant difference was observed for other independent variables: consumption of prescription medication (Roy’s greatest root = 0.0791, p = 0.0985), experiencing chronic illness (Roy’s greatest root = 0.0644, p = 0.2091), year of study (Roy’s greatest root = 0.069, p = 0.1795), field of study (Sciences vs. Non-Sciences, Roy’s greatest root = 0.0814, p = 0.0979), food preference (Roy’s greatest root = 0.0881, p = 0.0603). Hence, subsequent analysis was restricted to the above-mentioned four independent variables80.

We next applied the ANOVA technique to determine which of the dependent variables differed significantly based on gender, health insurance subscription, on-campus gym usage, and family income of the subjects (Table 3). Our results reveal that gender significantly influenced the SS scale (p = 0.029), although effect sizes were minimal to small (see Discussion). At the same time, the presence or absence of a health insurance subscription significantly impacted all scales (p < = 0.036) except AMH (Table 3). A moderate effect size was observed for NHS, SS, and HPS scales, while the effect sizes were smaller for all other scales (HSI, CA, AE, UHI, and FHI). Similarly, the use of on-campus gymnastic facilities significantly influenced HSI, AMH, AE, NHS, and UHI (p < = 0.036) scales, with a substantial effect observed for AMH and a smaller effect for the other four scales. Family income of the subjects had a significant effect on all scales (p < = 0.026) except UHI and FHI (Table 3). Concerning effect sizes, a more significant effect was observed for HPS, a moderate effect for HSI, SS, and NHS, and a small effect for the remaining scales (AMH, CA, AE).

Table 3 Results of univariate analysis of variance (ANOVA).
Full size table

We next performed a follow-up analysis to determine the nature of differences in health literacy between various levels of the independent variables employed in the study (Table 4). We performed t-tests (two-tailed, unpaired) to measure the significance of differences between various levels of the independent variables and quantified the effect size using Cohen’s D metric (Table 4). With respect to gender, female participants scored better than male participants on the SS scale (p = 0.029). Students who had a health insurance subscription fared significantly better than those who didn’t on most scales of the HLQ (p < = 0.036, Table 4). Students who utilized on-campus gym facilities scored high on multiple dimensions of health literacy (HSI, AMH, AE, NHS, and UHI, p < = 0.036) compared to students who didn’t utilize such facilities (Table 4). Lastly, with respect to family income, our results indicate that higher household income correlates with better health literacy on multiple dimensions (Table 4). Specifically, group 1 participants (Income < INR 90000) fared worse than group 2 (Income between 90000 and 2 lakh INR) on the SS scale (p = 0.012) and group 3 subjects (Income > 2 lakh INR) on HPA, HSI, SS, and NHS scales (p < = 0.027). Group 3 participants fared better than group 2 participants in HPA, HSI, AMH, CA, AE, and NHS scales (p < = 1.1e-3, Table 4). The effect size for G1 vs. G3 comparisons was substantial for the HPS, HSI, and SS scales, while a moderate effect size was observed for the NHS scale. For G2 vs. G3 comparisons, moderate effect sizes were observed for all statistically significant scales. For G1 vs. G2, the effect size was moderate to large for the SS scale. Our results reveal that various demographic factors influence college students’ health literacy, which may have broader implications for health promotion programs.

Table 4 Results of follow-up analysis summarising the values of nine HLQ scales representing the various sociodemographic groups of subjects in the study.
Full size table

Discussion

We believe that this is the first study that measures the multidimensional aspects of health literacy among college students in India. Studies of this kind are pivotal to bridge the literacy gap between healthcare providers and ordinary people through health policy, nurturing, and awareness to build a robust healthcare system in the country. Our research aims to identify gaps in health literacy among college students, which could lead to the development of academic intervention programs and policy measures to help students manage their health successfully. To this end, we utilized a well-validated, widely used, and multidimensional tool to quantify health literacy5. In addition to understanding the multidimensional properties of health literacy, our study identifies the various social and demographic parameters that influence college students’ health literacy. The study reveals that the health literacy of college students is influenced by their gender, the economic conditions of the family, physical activity (on-campus gym), and subscription to health insurance.

Health literacy of college students in India

We first wanted to compare the health literacy of students reported in our study with that of the general population in India. Although large-scale health literacy studies in India are hard to find, a previous study57 estimated the health literacy of members of a university’s health sciences and non-health sciences departments using the HLQ tool5. Compared to this study57, the scores on nine scales of HLQ among college students in our study were generally lower, especially when compared to health sciences subjects. Similar trends were also seen when our study’s health literacy scores were compared to those of students enrolled in healthcare-related programs in the Danish42 and Australian46 studies. However, the scores of students in our study are comparable to those observed in university-wide studies in the United States (Texas)43 and Jordan48 that spanned students from all disciplines and not just students enrolled in health-related courses. Students pursuing health-related programs tend to have better health literacy scores since they can better understand, assimilate, and process health information through their curriculum39,42,46,48,57. Differences were also noticed when the health literacy scores were compared between students enrolled in different health-related courses (medical, nursing, public health)42,46. Similar to the results of the study by Cuthino et al.57. the subjects in our study scored highest on the SS scale among the other 4-point scales of the HLQ. Lastly, the health literacy levels reported in our study are higher compared to another study where subjects in rural India were surveyed60.

Adequate caution should be exercised since this comparative observation doesn’t include statistical testing. Data comparisons and statistical testing across cross-sectional studies are challenging because of the differences in sampling methodologies and data acquisition techniques. This might result in different sample characteristics preventing meaningful comparison and statistical testing across studies. Further, comparisons between two cross-sectional studies could be complicated due to changes in population characteristics in the intervening period. Selecting appropriate statistical techniques, open data sharing in public repositories, and addressing possible confounders are other factors that must be considered to facilitate comparisons across cross-sectional studies81. Therefore, future implementations of HLQ in the Indian setting might want to incorporate longitudinal studies of health literacy among the student population and systematic comparisons of health literacy between college students and the general population. We believe that our study could be the first step towards large-scale surveys of health literacy among college students in India.

As mentioned earlier, among scales 1–5, the highest scores were recorded on the social support for health scale. In this regard, our results have been reinforced in health literacy studies of Nepalese2Jordanian48Danish42and American college43 students. Indian students could have increased social support for health owing to the socialistic and collectivistic identity of Indian society and the presence of strong interpersonal bonds among college students. As a result, students could seek social support from their friends, families, and other students to effectively manage their health. More importantly, the high perceived social support among subjects in the study involving Nepalese students82 implies the importance of family, friends, and community support at times of health needs. Similarly, social support from friends and families among Latinos is known to be positively correlated with physical and mental health, reinforcing the collectivist nature of the Latino community83,84,85. Also, support from family and friends was highly associated with life satisfaction in a sample of Spanish students86. These results collectively emphasize the importance of cultural values on social support, which can positively affect well-being.

Among scales 6–9, our study reveals that the participants scored highest on the scale ‘Ability to actively engage with healthcare providers (AE)’. This contrasts with other studies exploring health literacy among college students, where scales UHI2,46 and FHI2,42,48 were reported to have higher scores. This shows that subjects in our study can have active discussions with healthcare providers and obtain helpful information regarding their health, a result that was observed in a previous study57, administering HLQ in Indian settings. This could be due to the ease of access to healthcare facilities in urban settings, the availability of campus physical and mental health support systems, and the presence of high baseline knowledge about health among college students. Also, there is a more significant need to engage with healthcare providers as college marks a transition in a student’s academic journey, shifting their physical, mental, and nutritional habits.

Sociodemographic determinants of health literacy

In our study, certain sociodemographic variables significantly influenced the health literacy of the subjects. We probed whether students pursuing physical activity at the on-campus gymnastic facilities had varied levels of health literacy compared to those who weren’t physically active. Our results reveal that students pursuing physical activities had higher health literacy in five scales (HSI, AMH, AE, NHS, and UHI) compared to the other group. Our results were corroborated in a study of German university students39,87 and multiple other studies probing the relationship between health literacy and physical activity88. Potential reasons for the association between gym usage and health literacy are as follows: First, students who use the gym and engage in physical activities could be inherently mindful of their health, exhibit greater knowledge of health promotion activities, and demonstrate better health literacy. Second, students who use these facilities could be exposed to health-promoting information through discussions with their peers/trainers, which could improve their basic understanding of health promotion and literacy. Hence, a bidirectional relationship exists between health literacy and engaging in physical activity. It is noteworthy that enhanced health literacy could lead to a better understanding of how physical activity benefits one’s health and prevents cardiovascular diseases89.

Similar to other studies in the literature43,74,75,90our study reports that health literacy is positively correlated with economic status. In our study, higher family income is associated with better health literacy in almost all scales except UHI and FHI. G3 members with a family income greater than INR 200,000 seem to exhibit better health literacy compared to G2 (INR 200000 > family income > 90000) or G1 (family income < INR 90000) members. Our findings are significant since low socioeconomic status is correlated with poorer health outcomes91. In addition to family income, students who subscribed to a health insurance policy appeared to be more literate concerning matters pertaining to health than those who did not.

We did not find a significant effect of the consumption of prescription medications on the health literacy of the subjects. This finding was corroborated in a previous study involving college students in Denmark42. Possible reasons for the absence of the effect could be a blurred distinction between prescription and over-the-counter drugs in India92. Additionally, self-medication practices93 could prevent obtaining help from a licensed medical practitioner, which in turn could affect health literacy. Unlike our study, the same Danish study42 reported that students experiencing chronic illness had decreased social support for health, and similar results were also reported in another study94. Potential explanations for the absence of a difference in health literacy between subjects with and without chronic illness could include the diverse list of chronic diseases included in the study, which, along with different severity levels, compensatory mechanisms, and management practices, variably impact the health literacy of the subjects. Additionally, college students represent a homogenous population with high baseline health literacy levels, which could probably explain the absence of the effect of prescription medications and chronic illness in the study. In addition to prescription medication and chronic illness, we did not notice any effect of the year of study or the discipline of study on health literacy. Concerning the discipline of study, we did not observe a significant difference in health literacy between science vs. non-science students or biology vs. non-biology students (data not shown). Several studies in the literature have demonstrated that the health literacy of students varies based on the discipline of study42,46.

In the study, we observed borderline significant p-values slightly greater than the alpha value of 0.05 (95% confidence level) for specific comparisons (Table 3). In all those cases, we concluded that our statistical test failed to reject the null hypothesis. However, these significant borderline cases could be due to inadequate sample size or a weak or modest association between the predictor and dependent variables. Specifically, many borderline significant p-values (if not all) were observed for the effect of gender on health literacy (HPS, p-value = 0.05, AMH, p-value = 0.064, see Table 3). All these cases were characterised by a wide confidence interval ranging from zero to medium effect, indicating uncertainty, which may necessitate statistical testing with an optimal sample size95. Additionally, a careful analysis of the literature indicates that the effects of gender on the health literacy of college students are mixed. While the studies on Danish42 and American43 students report higher literacy for females compared to males, the opposite was observed in reports involving Nepalese2 and Chinese74 student cohorts. Even though the studies that probe the health literacy of Danish and American students agree that female students tend to possess better health literacy than males on average, they differ on the results of individual scales. Similar to our study, the Danish42 study reported that female students tend to have better social support for health than males, a result not seen in the study involving American students43. While these differences could arise from cultural dissimilarities between the study populations, these findings further affirm the small effect size of gender on health literacy.

Implications for health literacy promotion and policy

Our study indicates the presence of significant disparities in health literacy across various student categories and groups. As poor health literacy is positively correlated with risky behaviours8increased hospitalizations, and adverse health consequences8,10,11,12education experts must understand the gaps in students’ health literacy adequately. We believe that addressing the gaps in health literacy at an earlier stage in students’ careers would lead to better assimilation and application of health-related knowledge that they acquire later in life. Together, this would lead to a healthy environment across university campuses.

Considering the overall health of the student community, the HLQ5 could be systematically administered to understand the deficiencies of health literacy among students. Based on the observations, sufficient intervention programs could be implemented that could use the multidimensional constructs of the HLQ5 and the various factors that determine health literacy. These intervention programs could take the form of adding modules to the existing courses in the curriculum or devising new courses that address gaps in health literacy. In addition to addressing general health literacy, the programs could focus on specific topics such as nutritional health, infectious diseases, mental health, gut health, and chronic conditions. These curricular interventions could be supplemented by workshops and masterclasses on health literacy from healthcare institutions and non-governmental organizations.

Lastly, one of our important results is the presence of low health literacy among students with low family income. Health literacy promotion programs could systematically help bridge the deficiencies in this stratum of the student population. Effective intervention programs could be designed using the effect sizes reported in the study, where the training modules’ duration and/or intensity could be adjusted based on the effect sizes. For example, moderate to large effect sizes were observed for scales 1, 2, 4, and 7 (HPS, HSI, SS, and NHS). Training modules can be designed so that groups that scored less on those scales (Table 4) could be provided more rigorous training by adjusting the duration and/or intensity. Another way of implementing this is by using generative artificial intelligence techniques where the learning module can adaptively adjust the duration and/or intensity of the training about the different health literacy components based on the subjects’ proficiency. On the other hand, a smaller but statistically significant effect could mean that intervention programs might need to look beyond the determinant of interest. For example, family income has a small but significant effect on the appraisal of health information (scale 5). Intervention programs that aim to improve this aspect of health literacy might have to look beyond the family income of the subjects. Other determinants could include cultural and linguistic factors that may affect how students critically appraise health information when presented to them. All these measures would collectively build student health empowerment, contributing to their long-term professional and personal well-being.

Limitations of the study and future work

One of the inherent limitations of our study is the purposive sampling method71 that we employed to encourage participation from all disciplines and years of study. While this strategy undoubtedly encouraged participation from all sections of the student population, it could hamper the generalizability of the study’s findings to a broader community of college students in India. With this sampling approach, we could have oversampled subjects with high health literacy due to selection bias, where students with a deep interest in health-related topics are more likely to participate in the study, thereby underreporting the effects of various sociodemographic factors on health literacy. Although this non-random sampling strategy is commonly employed in health literacy studies involving university students42,46,48,57and we managed to enhance student participation by circulating the expression of interest and emphasizing the importance of the project multiple times via email, adequate caution needs to be exercised and the study results may not generalize to a wider student population. Future health literacy measurements could include probabilistic sampling methods96 to ensure uniform representation across all strata, which could support strong generalization of the results to a wider student population in India.

The study suffers from another limitation: the exclusion of the third-gender community. The third-gender community was excluded from the study due to an insufficient sample size, which could limit their expertise and knowledge of health literacy. While the study results offer genuine insights into the health literacy profiles of college students in India, they may not wholly generalize to the third-gender community and do not reflect their health literacy needs. This could hamper the thoroughness and inclusiveness of our results. Therefore, targeted research studies that focus on health literacy of the third gender community are required to address this drawback and make sure that health literacy policy frameworks are established based on equitable principles.

We utilized the English version of the HLQ5 to measure college students’ health literacy easily. A potential limitation of the study could be that the HLQ, though known to possess robust reliability and validity among subjects worldwide2,8,57,60,97was not culturally adapted to the Indian context. This may lead to different interpretations of specific questions in the HLQ among various cultural and linguistic groups. Further, specific nomenclature in the HLQ may not accurately reflect the design of healthcare systems in India. Future studies could be conducted by adapting the HLQ culturally and linguistically to aid generalization in a broader Indian context.

We did not statistically adjust for the potentially confounding effect of different variables (age, gender, etc.) in the study. The primary goal is to examine disparities in health literacy across various sociodemographic groups among college students. We don’t aim to decipher any causal relationships among the variables, and the study is purely exploratory. Therefore, the results should be interpreted with adequate caution as potential unmeasured confounders could influence the associations reported in the study. For example, while it’s true that family income significantly affects the health literacy of the subjects on multiple scales in the study, it is possible that potential confounding variables could mediate the relationship between the two. Future studies could be designed to control for the confounding effects of parental education and employment, community and peer networks, availability of healthcare and medical services, childhood health status, job market conditions, and genetics to decipher the accurate relationship between family income and health literacy39,98. Similarly, the relationship between health insurance/physical activity and health literacy could be confounded by variables such as family income, parental education, social support, etc.

In the future, we plan to model the relationship between health literacy and different wellness measures among the college student population. Such additional measures of wellness may include gastrointestinal wellness (gastrointestinal quality of life)99mental health100and other physical metrics such as changes in blood pressure, body weight, and sleep patterns over time. In addition, it would be helpful to understand the correlations between health literacy among college students and patient satisfaction, the rate of emergency hospital visits, and the duration of hospital stay. These explorations would help decode the effect of health literacy on health consequences and aid in devising intervention strategies at an early stage of a person’s life.

Conclusion

There needs to be more research conducted on the health literacy of college students in India. This study aims to understand college students’ needs and barriers to health literacy, which may be the first step in creating populations that possess health literacy across college campuses. Our results indicate the presence of disparities in various dimensions of health literacy across different student groups. The findings of this study could motivate academic administrators and professionals to develop curricular incorporations of health literacy modules, health literacy workshops, and specific training modules catered to the needs of various student groups. These changes could promote health literacy and contribute to the overall well-being of the student community.

Methods

Study design

Our study aims to understand health literacy among college students in India and quantify how demographic and health-related information of students affects their health literacy. This study employed a cross-sectional approach and was carried out at a university in southern India. The participants are students pursuing college degrees in science and non-science disciplines. The respondents were requested to participate in the study after obtaining clearance from the Institutional Review Board of Krea University (approval granted on 17/11/2022). An email with an expression of interest form was sent to all students regarding the project, and data was collected from those who expressed an interest in participating in the research study. The students who participated in the study come from all four years of study (first, second, third, and fourth/post-graduate diploma) and various disciplines ranging from mathematics to political sciences. The post-graduate diploma is an additional year of training for the students that will enable them to deepen their knowledge in the subject, do research projects, or focussed writing on a theme supported by relevant methodological skills. The participants were briefed about the study, and informed consent to participate was obtained before carrying out the survey. The surveys in this study were administered anonymously, and no identifiable information was collected from the participants. All methods were performed according to the guidelines set by the university’s institutional review board.

Data collection

Health literacy was measured using the well-established Health Literacy Questionnaire (HLQ) developed by Prof. Richard Osborne and colleagues2,5,46. The HLQ was previously shown to exhibit robust reliability and validity measures among diverse subjects worldwide2,57,60,97,101,102. We used a purposive sampling method71 to increase the participation of students from all years of study (first, second, third, and fourth/post-graduate diploma) and different majors of study. The HLQ2,5 consists of a 44-item questionnaire measuring nine health literacy scales among the respondents. While some of the scales (the first five scales) include four items, the other scales have five items (the last four scales). The nine scales of HLQ can be found in Table 1. The outcome of the administration of HLQ is not a single composite score but a score for each of the nine scales.

In addition to HLQ, sociodemographic information was also collected from all the respondents, which includes the following: gender, field of study, year of study, prescription medication, health insurance, nationality, family income, food preference, and physical activity status through the use of the on-campus gym. These factors were chosen based on previous studies that explored the relationship between college students’ health literacy and various sociodemographic and behavioural factors42,43,48,57.

The researchers ensured the sample was as representative as possible compared to the population size. The optimal sample size was established considering a 95% confidence level and 6% margin of error using Cochran’s formula for finite populations103. An a priori power analysis was conducted with the following parameters using the G*Power software104: α = 0.05 (error probability), f2 = 0.15 (effect size) to determine the sample size required to achieve a power of 0.8. The minimum required sample size was computed for three values of the number of groups (ng = 2, 3, and 4) corresponding to levels of independent variables in the study. This analysis revealed that the minimum sample size to achieve the desired power (0.8) is 114 (ng = 2), 75 (ng = 3), and 60 (ng = 4). The same analysis repeated for higher power (0.95) is 166, 108, and 84, respectively. Therefore, the sample size employed in the study is sufficient to achieve the desired statistical power. We accepted participation from all sections of the student population, irrespective of any course enrolled or year of study. We did not exclude any student who expressed a willingness to participate in the study. The data collection was performed over a period of 17 weeks from December 2022 to March 2023.

Data analysis

All analysis in the study was performed using the Google Collaboratory application (https://colab.research.google.com/) in Python programming language (https://www.python.org/). To aid scientific reproducibility, all analyses and data files would be provided to the research community upon reasonable request. After data collection from the participants, descriptive statistics of all dependent variables (HLQ scales) were computed and visualized utilizing appropriate Python libraries.

Multivariate analysis

To decipher the effect of sociodemographic factors on the different scales of the HLQ, we resorted to multivariate (MANOVA) and univariate analysis of variance (ANOVA)80. We first employed MANOVA to understand which of the sociodemographic variables of interest had a significant effect on the HLQ scales. Before carrying out MANOVA, the assumptions were validated using Pearson’s correlation coefficient between the dependent variables and the application of Levene’s test105,106. All dependent variables (and their residuals) were normally distributed (Additional file 2) and exhibited a high positive correlation among each other, with Pearson’s correlation coefficient ranging from 0.26 to 0.75 106 (Additional file 1). Homogeneity or equality of variance between groups was established using Levene’s test (p-values ranging from 0.36 to 0.99). Since a few independent variables had more than two separate groups and due to the observation of unequal sample sizes among different variables, Roy’s Largest Root, among multiple other statistics, was chosen to establish the outcome of testing using MANOVA (95% confidence level)48. No interaction effects were modeled owing to the sample size limitations of the study.

Univariate analysis

Based on the results of the MANOVA test in the previous step, ANOVA (95% confidence level) was carried out for independent variables that exhibited a significant effect on the dependent variables. We resorted to this procedure of MANOVA followed by multiple ANOVAs to control the type 1 error rate in behavioural sciences studies80. The effect sizes were calculated using partial Eta squared (η2) and Cohen’s F statistical metric (small = 0.1, medium = 0.25, and large = 0.40)107.

Post-Hoc analysis

In addition to performing ANOVA, additional follow-up analysis using two-tailed t-tests (95% confidence level)108,109,110,111, was performed to better quantify the source of differences in the HLQ scales among different groups of independent variables (sociodemographic factors). The effect sizes in the follow-up analysis were determined using Cohen’s D metric107.