Abstract
Background
This study aimed to examine the associations of digital health information acquisition (based on Web 1.0 and Web 2.0) and digital verification behavior with health-seeking behavior among university students.
Methods
This cross-sectional study was conducted between February and March 2026 among 302 university students selected using a convenience sampling method. Data were collected through face-to-face questionnaires administered in classroom settings. The survey consisted of a Personal Information Form, the Digital Health Information Acquisition and Verification Scale, and the Health-Seeking Behavior Scale. Data were analyzed using IBM SPSS Statistics 26 software. Descriptive statistics were used to summarize participant characteristics and study variables. Pearson correlation and regression analyses were conducted to examine the associations among the study variables and their contributions to health-seeking behavior.
Results
The findings indicate that there is a positive and significant relationship between digital health information acquisition and verification levels and health-seeking behavior (r = .422; p < .001). The results of the simple regression analysis revealed that digital health information acquisition and verification was significantly associated with health-seeking behavior (R² = 0.178; p < .001). According to the hierarchical regression results, Web 1.0-based health information acquisition emerged as the strongest predictor of health-seeking behavior. Although Web 2.0-based information acquisition and digital verification contributed significantly to the model, their incremental contributions to explained variance were relatively small (final model R² = 0.206; p < .001).
Conclusion
Digital health information acquisition and digital verification behavior were significantly associated with health-seeking behavior among university students. The findings suggest that access to digital health information and the tendency to verify information may be related to health-seeking behavior. However, the explanatory power of the model was modest. In addition, the ability to critically evaluate and verify information may play a supportive role in health-related decision-making in digital environments, although its unique contribution to explaining health-seeking behavior was relatively modest.
Keywords: Digital Health Information, Web 1.0, Web 2.0, Digital Verification, Health-Seeking Behavior, University Students
Introduction
With the acceleration of digitalization, the ways of accessing health information have undergone a profound transformation. The internet, mobile applications, social media platforms, and artificial intelligence–supported digital tools have become primary sources for individuals to obtain health-related information [1, 2]. Today, individuals not only seek information about symptoms and treatment options but also increasingly base their decisions regarding health professionals, healthcare institutions, and preventive health practices on information obtained from digital environments [3, 4]. This transformation has made digital health information acquisition a critical determinant shaping health-related behaviors [1, 5].
The process of digital health information acquisition is frequently addressed in the literature within the framework of Web 1.0 and Web 2.0–based platforms. Web 1.0 refers to environments such as institutional websites, online news portals, and static information pages where users primarily act as passive recipients of information. In contrast, Web 2.0 encompasses dynamic and interactive platforms characterized by user-generated content, including social media platforms, online forums, blogs, and video-sharing sites. The fundamental distinction between these two structures lies in the modes of information production and dissemination. In Web 2.0 environments, users assume the roles of content creators and commentators, thereby accelerating the dissemination of health information and increasing its impact [4, 6, 7]. In this context, the structural differences between Web 1.0 and Web 2.0 environments differentiate individuals’ ways of accessing digital health information and their processes of evaluating such information [1, 2]. Although Web 1.0 and Web 2.0 are often discussed as conceptually distinct environments, the boundaries between these platforms have become increasingly blurred in contemporary digital ecosystems. Many institutional health organizations now actively disseminate information through social media platforms, while user-generated content may also incorporate information originating from official sources. Therefore, differences between Web 1.0 and Web 2.0 should not be interpreted as a simple distinction between reliable and unreliable information environments. Rather, these dimensions represent different modes of information production, dissemination, and interaction that may influence how individuals access, evaluate, and use health information. Studies on digital health literacy emphasize the cognitive and behavioral skills required to navigate these environments, including the evaluation of the reliability and relevance of online content [3–5, 8].
Health-seeking behavior is a multidimensional construct that encompasses the processes through which individuals recognize a health need, search for information, evaluate available options, and act accordingly [9–11]. In the literature, it is widely accepted that this behavior is influenced by perceived risk, health literacy, self-efficacy, prior experiences, and sociodemographic factors [1, 12, 13]. However, with the expansion of the digital health ecosystem, the dynamics of health-seeking behavior have also evolved. Traditional face-to-face consultation processes are increasingly supported by, or even replaced by, online information-seeking practices [1, 4]. While this transformation enables individuals to make health decisions more rapidly, it also requires them to operate within a more complex and uncertain information environment [4, 5].
The process of digital health information acquisition provides individuals with time- and location-independent access, increased health awareness, and the opportunity to take a more active role in managing their own health [1, 3]. However, digital environments also present risks such as information overload, unverified content, and conflicting health messages, which may negatively affect health-seeking behavior [2, 5, 12]. Particularly in Web 2.0 environments, the rapid dissemination of user-generated content can make it difficult to assess the accuracy of information. This situation renders digital verification behavior may represent an important factor in health-related decision-making processes. Digital verification behavior refers to the process through which individuals evaluate the credibility, accuracy, and reliability of health information obtained from digital sources before accepting or using it. This process may involve comparing information across multiple sources, checking official health websites, consulting healthcare professionals, or assessing the source of the information itself. Within the framework of digital health literacy, verification behavior is considered an essential skill that helps individuals navigate complex online health information environments and reduce the risk of misinformation [8, 12]. Incorrect or incomplete digital health information may lead to unnecessary anxiety, excessive use of healthcare services, or avoidance of professional support [5, 8, 12]. One of the groups most affected by this transformation is university students, who represent young adults. Young adults constitute a population that is fully immersed in digital technologies, spends a significant amount of time in online environments, and primarily relies on digital platforms for information acquisition. The university period also represents a critical developmental stage in which individuals begin to make independent health-related decisions and assume greater responsibility for their own health [1, 2, 4]. Therefore, exposure to digital health information may play a particularly influential role in shaping the health-seeking behaviors of young adults.
Although previous studies have examined digital health literacy, online health information seeking, and health-related behaviors, relatively fewer studies have simultaneously examined Web 1.0-based information acquisition, Web 2.0-based information acquisition, and digital verification behavior within a single analytical framework. In addition, limited attention has been given to the differential associations of these dimensions with the subdimensions of health-seeking behavior [1–4, 12]. Therefore, the present study aims to extend existing research by examining these associations among university students.
In this regard, the main research questions of the study are formulated as follows:
What are the relationships between Web 1.0-based health information acquisition, Web 2.0-based digital health information acquisition, digital verification behavior, and health-seeking behavior and its subdimensions?
To what extent is Web 1.0-based health information acquisition associated with health-seeking behavior?
To what extent is Web 2.0-based digital health information acquisition associated with health-seeking behavior beyond Web 1.0-based health information acquisition?
To what extent is digital verification behavior associated with health-seeking behavior beyond Web 1.0- and Web 2.0-based health information acquisition?
By addressing digital health information acquisition within a comprehensive model that includes Web 1.0, Web 2.0, and digital verification dimensions, this study seeks to contribute to the existing literature. The findings are expected to enhance understanding of health-related decision-making processes among young adults in the digital health era and provide a theoretical and practical basis for developing digital health literacy–based interventions targeting university students.
Method
Research design
This study employed a quantitative cross-sectional design to examine the relationships between digital health information acquisition, digital verification behavior, and health-seeking behavior among university students. Data were collected between February and March 2026.
Population and sample
The population of the study is limited to approximately 600 students aged 18 and over who are enrolled at the Health Services Vocational School of Mustafa Kemal University in Hatay, Türkiye. Data were collected using a face-to-face survey method, and participation was entirely voluntary. Convenience sampling was preferred for sample selection. The primary reason for choosing convenience sampling was the limited access to the population and the fact that the study was conducted within a specific time frame. The study population consisted exclusively of students enrolled in two-year associate degree programs at the Health Services Vocational School of Mustafa Kemal University. Therefore, only first- and second-year students were eligible for participation.
However, the relatively large sample size and the inclusion of participants from different programs within the vocational school may have contributed to the robustness of the statistical analyses. Nevertheless, the findings should be interpreted with caution due to the non-probability sampling approach and the limited study setting. Considering that the population size was approximately 600 students, the minimum required sample size was calculated based on a 95% confidence level and a 5% margin of error using the sample size table proposed by Yazıcıoğlu and Erdoğan [14] and Gürbüz and Şahin [15]. According to this calculation, a minimum of 234 participants was required to adequately represent the target population. To increase the reliability of the findings and compensate for potential missing or incomplete responses, data collection continued throughout the study period. As a result, questionnaires were obtained from 302 students, exceeding the minimum sample size requirement. Therefore, the final sample exceeded the minimum sample size requirement and was deemed adequate for the planned statistical analyses.
Data collection instruments
Research data were collected between February and March 2026 using a face-to-face survey method. The survey was administered in a classroom setting, and participants were provided with the necessary information regarding the purpose of the study.
A questionnaire consisting of three sections was used to measure the participants’ demographic characteristics and research variables. This form included the Personal Information Form, the Digital Health Information Acquisition and Verification Scale, and the Health-Seeking Behavior Scale.
Personal information form
The Personal Information Form, developed by the researcher, was used to determine the demographic and individual characteristics of the participants. The form included sociodemographic variables such as age, gender, department of study, class level, and place of residence prior to university enrollment. In addition, a question regarding daily internet usage time was included to assess digital technology use.
Digital Health Information Acquisition and Verification Scale
In the study, the “Digital Health Information Acquisition and Verification Scale,” developed by Çömlekçi and Bozkanat [7], was used to measure participants’ levels of acquiring and verifying health information in digital environments. The scale consists of 10 items and three subdimensions. Specifically, 3 items assess health information acquisition through Web 1.0-based sources, 3 items assess health information acquisition through Web 2.0-based platforms, and 4 items assess digital verification behaviors related to health information obtained from digital environments. The Web 1.0 subdimension assesses the extent to which individuals obtain health information from relatively structured and institution-based digital sources, such as official health websites, online news portals, and informational webpages. The Web 2.0 subdimension evaluates health information acquisition through interactive and user-generated platforms, including social media, online forums, blogs, and video-sharing platforms. The digital verification subdimension measures the tendency of individuals to evaluate the accuracy and credibility of health information by comparing information across sources, checking official resources, and verifying content before accepting or using it. The items are arranged on a 5-point Likert scale, and participants are asked to evaluate each statement between “1-Never” and “5-Always.” Scale scores are calculated using the arithmetic mean method. A score approaching 5 indicates a high level of digital health information acquisition and verification, whereas a score approaching 1 indicates a low level. The scale does not contain any reverse-coded items. In the original study, the Cronbach’s Alpha coefficient was reported as 0.758. In the present study, the reliability analysis yielded a Cronbach’s Alpha coefficient of 0.845. This value suggests that the scale demonstrated acceptable internal consistency within the context of the present study [16].
Health-Seeking Behavior Scale
The “Health-Seeking Behavior Scale,” developed by Kıraç and Öztürk [9], was used to measure participants’ health-seeking behaviors. The scale consists of 12 items and three subdimensions (online health seeking, professional health seeking, and traditional health seeking). The online health-seeking subdimension assesses individuals’ tendency to search for health-related information through internet-based sources and digital platforms. The professional health-seeking subdimension evaluates the tendency to seek support from healthcare professionals, such as physicians and other qualified healthcare providers, when faced with health concerns. The traditional health-seeking subdimension measures the tendency to rely on non-professional and conventional sources, including advice from family members, friends, or commonly practiced traditional approaches when making health-related decisions. The items are structured on a 5-point Likert scale, and participants evaluated the statements between “1-Strongly disagree” and “5-Strongly agree.” Scale scores are calculated using the arithmetic mean method. A score approaching 5 indicates a high level of health-seeking behavior, whereas a score approaching 1 indicates a low level. The scale does not include any reverse-coded items. In the original study, the Cronbach’s Alpha value was reported as 0.755. In the present study, the internal consistency coefficient of the scale was calculated as 0.710. This value suggests that the scale demonstrated acceptable internal consistency within the context of the present study [16].
Data collection
The data collection process for this study was conducted in a single stage between February and March 2026. Data were collected through a face-to-face survey method administered in a classroom setting at the Health Services Vocational School of Mustafa Kemal University in Hatay, Türkiye.
At the beginning of the data collection process, participants were informed about the purpose of the study and the voluntary nature of participation. Confidentiality and anonymity were assured throughout the research process.
To recruit participants, permission was first obtained from the relevant department heads and course instructors. Data collection was carried out in regularly scheduled classroom sessions across all academic programs within the vocational school. During the February–March 2026 data collection period, a total of 14 classrooms representing first- and second-year students were visited. Each classroom was visited once, and students were informed about the purpose, procedures, and voluntary nature of the study before being invited to participate. A total of 354 students were approached, of whom 302 agreed to participate and completed the questionnaire, corresponding to a response rate of 85.3%. Fifty-two students declined participation, while some students were absent on the days of data collection. All completed questionnaires were reviewed, and no questionnaires were excluded due to missing or incomplete responses.
The primary inclusion criteria for participation were being 18 years of age or older and currently enrolled as a student at the relevant institution. Although a small number of students under the age of 18 were enrolled in some first-year programs, these students were not included in the study. The age criterion was established to ensure that all participants were legally able to provide informed consent independently and to avoid the additional ethical and administrative procedures required for research involving minors, such as obtaining parental or guardian consent. Therefore, only students aged 18 years and above were considered eligible for participation. No additional screening criteria were applied, as the study aimed to capture general digital health information acquisition and health-seeking behaviors among university students.
The questionnaire was administered in a controlled classroom environment to ensure consistency in data collection conditions. Participants completed the survey individually, and no identifying personal information was collected in order to maintain anonymity. All collected data were used exclusively for research purposes and were not shared with any third parties under any circumstances.
Data analysis
The data obtained within the scope of the study were analyzed using IBM SPSS Statistics 26 software. First, the demographic characteristics of the participants (age, gender, department, class level, place of residence prior to university enrollment, and daily internet usage time) were evaluated using descriptive statistics through frequency and percentage distributions.
To determine the normality of the data distribution, skewness and kurtosis values were examined. The ± 1.5 range recommended by Tabachnick and Fidell [17] was taken as the criterion. It was found that the values for the Digital Health Information Acquisition and Verification Scale (Skewness = 0.200; Kurtosis = 0.257) and the Health-Seeking Behavior Scale (Skewness = -0.122; Kurtosis = 0.396) fell within this range. Based on these findings, it was accepted that the data met the assumption of normal distribution.
Since the normality assumption was satisfied, parametric tests were preferred in the analyses. Pearson correlation analysis was conducted to determine the relationships between variables. Simple linear regression analyses were performed to examine the effect of digital health information acquisition and digital verification levels on health-seeking behavior. Hierarchical linear regression analysis was applied to determine the incremental effects of independent variables on health-seeking behavior.
Hierarchical linear regression analysis was conducted to examine the incremental associations of digital health information acquisition and digital verification behavior with health-seeking behavior after controlling for demographic characteristics. In the first step, demographic variables (gender, age group, academic year, and daily internet use) were entered as control variables. Web 1.0-based health information acquisition was entered in the second step, Web 2.0-based digital health information acquisition in the third step, and digital verification behavior in the fourth step. In addition, regression assumptions were evaluated using Durbin–Watson statistics, variance inflation factor (VIF), and tolerance values. Thus, the relative and incremental contributions of these variables in explaining health-seeking behavior were tested. In addition, regression assumptions were further evaluated by examining scatterplots of standardized residuals and predicted values, normal probability (P–P) plots, histograms of standardized residuals, Cook’s distance values, and Mahalanobis distances. The results indicated that the assumptions of linearity, homoscedasticity, normality of residuals, and the absence of influential outliers were adequately satisfied. A 95% confidence level was adopted in evaluating the research findings, and the level of statistical significance was accepted as p < .05.
Ethical aspects of the research
This study was conducted in accordance with the principles of scientific research and publication ethics. Ethical approval was obtained from the Social and Human Sciences Scientific Research and Publication Ethics Committee of Hatay Mustafa Kemal University with the decision dated 06.02.2026 and numbered 28. Participation in the study was voluntary, and informed consent was obtained from all participants. The consent process was ensured through the information text included at the beginning of the questionnaire, and participants were informed about the purpose, scope, and confidentiality principles of the study before completing the survey. The research process was conducted in accordance with the ethical principles applicable to studies involving human participants and in compliance with the Declaration of Helsinki.
Results
This section presents the descriptive statistics, correlation analyses, and regression findings regarding digital health information acquisition, digital verification behavior, and health-seeking behavior.
Table 1 shows the demographic characteristics of the participants. The sample was predominantly composed of female students and first-year students. Most participants were between 18 and 21 years of age and reported using the internet for 4–6 h per day.
Table 1.
Descriptive data on the demographic characteristics of the study group
| Demographic Characteristic | Groups | Number (n) | Percentage (%) |
|---|---|---|---|
| Gender | Female | 219 | 72.5 |
| Male | 83 | 27.5 | |
| Age | 18–19 years | 176 | 58.2 |
| 20–21 years | 92 | 30.5 | |
| 22 years and above | 34 | 11.3 | |
| Program of Study | Anesthesia | 38 | 12.6 |
| First and Emergency Aid | 58 | 19.2 | |
| Occupational Therapy | 30 | 9.9 | |
| Medical Documentation and Secretarial Studies | 43 | 14.2 | |
| Medical Imaging Techniques | 42 | 13.9 | |
| Medical Laboratory Techniques | 47 | 15.6 | |
| Elderly Care | 44 | 14.6 | |
| Class Level | First-year | 185 | 61.3 |
| Second-year | 117 | 38.7 | |
| Place of Residence | Village/Town | 48 | 15.9 |
| District | 136 | 45.0 | |
| City | 118 | 39.1 | |
| Time Spent on the Internet | 0–3 h | 52 | 17.2 |
| 4–6 h | 194 | 64.3 | |
| 7 h and above | 56 | 18.5 | |
| Total | 302 | 100.0 |
Table 2 presents the descriptive statistics of the study variables. Participants reported moderate levels of digital health information acquisition and verification (M = 2.55, SD = 0.74). Among the dimensions of digital health information acquisition, digital verification showed the highest mean score (M = 2.79), whereas Web 2.0-based digital health information acquisition had the lowest mean score (M = 2.21). Regarding health-seeking behavior, professional health seeking demonstrated the highest mean score (M = 4.14), followed by traditional health seeking (M = 3.35) and online health seeking (M = 3.00).
Table 2.
Descriptive statistics of the study variables
| Variable | Mean | SD | Min | Max |
|---|---|---|---|---|
| Digital Health Information Acquisition and Verification | 2.55 | 0.74 | 1.00 | 5.00 |
| Web 1.0-Based Health Information Acquisition | 2.57 | 0.88 | 1.00 | 5.00 |
| Web 2.0-Based Digital Health Information Acquisition | 2.21 | 0.96 | 1.00 | 5.00 |
| Digital Verification | 2.79 | 0.99 | 1.00 | 5.00 |
| Health-Seeking Behavior | 3.37 | 0.50 | 1.75 | 4.83 |
| Online Health Seeking | 3.00 | 0.72 | 1.00 | 5.00 |
| Professional Health Seeking | 4.14 | 0.71 | 2.00 | 5.00 |
| Traditional Health Seeking | 3.35 | 0.78 | 1.00 | 5.00 |
SD Standard Deviation
As presented in Table 3, digital health information acquisition and verification were positively associated with overall health-seeking behavior. However, the associations differed across the subdimensions of health-seeking behavior. Digital health information acquisition and verification showed the strongest relationships with online health seeking (r = .434, p < .001), whereas the relationship with professional health seeking was negligible and non-significant (r = .005, p = .932). In addition, Web 2.0-based digital health information acquisition demonstrated a small negative association with professional health seeking (r=-.140, p=.015). These findings suggest that the observed association with overall health-seeking behavior is driven primarily by the online health-seeking dimension. In addition, correlation analyses conducted for the subdimensions of health-seeking behavior indicated that the observed associations were primarily attributable to online health-seeking behavior, whereas professional health-seeking behavior demonstrated weak or non-significant relationships with several dimensions of digital health information acquisition.
Table 3.
Pearson correlation results for the scales and their dimensions
| Scales and Dimensions | Pearson Correlation Coefficients (r) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||
| 1. Digital Health Information Acquisition and Verification | r | 1 | 0.813** | 0.717** | 0.814** | 0.422** | 0.434** | 0.005 | 0.276** |
| p | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | 0.932 | < 0.001 | ||
| 2. Web 1.0 and health information acquisition | r | 1 | 0.514** | 0.486** | 0.403** | 0.457** | -0.004 | 0.193** | |
| p | < 0.001 | < 0.001 | < 0.001 | < 0.001 | 0.946 | 0.001 | |||
| 3. Web 2.0 and digital health information acquisition | r | 1 | 0.280** | 0.307** | 0.369** | − 0.140* | 0.232** | ||
| p | < 0.001 | < 0.001 | < 0.001 | 0.015 | < 0.001 | ||||
| 4. Digital verification | r | 1 | 0.302** | 0.243** | 0.113* | 0.222** | |||
| p | < 0.001 | < 0.001 | 0.05 | < 0.001 | |||||
| 5. Health-Seeking Behavior | r | 1 | 0.837** | 0.414** | 0.641** | ||||
| p | < 0.001 | < 0.001 | < 0.001 | ||||||
| 6. Online health seeking | r | 1 | 0.013 | 0.286** | |||||
| p | 0.82 | < 0.001 | |||||||
| 7. Professional health seeking | r | 1 | 0.127* | ||||||
| p | 0.027 | ||||||||
| 8. Traditional health seeking | r | 1 | |||||||
| p | |||||||||
n = 302
* p < .05
** p < .001
Table 4 presents the results of the simple linear regression analysis. Digital health information acquisition and verification showed a significant positive association with health-seeking behavior. The model explained a modest proportion of the variance in health-seeking behavior, suggesting that additional factors may also contribute to this outcome.
Table 4.
Findings regarding the effect of digital health information acquisition and verification on health-seeking behavior
| Variable | Unstandardized Coefficients | Standardized Coefficients | t | p | F | Model (p) | |
|---|---|---|---|---|---|---|---|
| B | SE | β | |||||
| Constant | 2.649 | 0.09 | 28.45 | < 0.001** | 65.17 | < 0.001** | |
| Digital Health Information Acquisition and Verification | 0.283 | 0.04 | 0.422 | 8.07 | < 0.001** | ||
R2 = 0.178
**p < .001
Regression Equation of the Model: Y = 2.649 + (0.283X)
B = unstandardized coefficient; SE = standard error; β = standardized coefficient; t = t-value; R² = coefficient of determination
The hierarchical regression results presented in Table 5 indicate that demographic characteristics (gender, age group, academic year, and daily internet use) were not significantly associated with health-seeking behavior in Model 1. After adjustment for these demographic variables, Web 1.0-based health information acquisition emerged as the strongest variable associated with health-seeking behavior (β = 0.252, p < .001). The addition of Web 2.0-based digital health information acquisition and digital verification behavior resulted in statistically significant but relatively small increases in explained variance (ΔR² = 0.016 and ΔR² = 0.015, respectively). The final model explained 20.6% of the variance in health-seeking behavior (R² = 0.206). Furthermore, multicollinearity diagnostics indicated no evidence of multicollinearity (VIF = 1.001–1.687; Tolerance = 0.593–0.999), and the Durbin–Watson statistic (1.744) suggested that the independence of residuals assumption was satisfied.
Table 5.
Hierarchical regression analysis of digital health information acquisition and digital verification predicting health-seeking behavior after adjustment for demographic characteristics
| Model and Variables | Regression Coefficients | Model Statistics | |||||||
|---|---|---|---|---|---|---|---|---|---|
| B | SE | β | t | p | R² | ΔR² | F | p | |
| Model 1 | |||||||||
| Constant | 3.437 | 0.15 | 22.44 | < 0.001** | 0.018 | - | 1.32 | 0.261 | |
| Gender | -0.109 | 0.07 | -0.098 | -1.69 | 0.091 | ||||
| Age Group | 0.062 | 0.05 | 0.085 | 1.34 | 0.181 | ||||
| Academic Year | 0.013 | 0.07 | 0.013 | 0.20 | 0.843 | ||||
| Daily Internet Use | -0.020 | 0.05 | -0.023 | -0.41 | 0.684 | ||||
| Model 2 | |||||||||
| Constant | 2.822 | 0.16 | 17.34 | < 0.001** | 0.175 | 0.157 | 12.56 | < 0.001** | |
| Gender | -0.074 | 0.06 | -0.066 | -1.24 | 0.215 | ||||
| Age Group | 0.065 | 0.04 | 0.089 | 1.53 | 0.127 | ||||
| Academic Year | 0.003 | 0.06 | 0.003 | 0.05 | 0.961 | ||||
| Daily Internet Use | -0.019 | 0.04 | -0.022 | -0.42 | 0.672 | ||||
| Web 1.0-Based Health Information Acquisition | 0.225 | 0.03 | 0.398 | 7.52 | < 0.001** | ||||
| Model 3 | |||||||||
| Constant | 2.790 | 0.16 | 17.23 | < 0.001** | 0.191 | 0.016 | 11.62 | < 0.001** | |
| Gender | -0.090 | 0.06 | -0.081 | -1.52 | 0.129 | ||||
| Age Group | 0.063 | 0.04 | 0.086 | 1.49 | 0.136 | ||||
| Academic Year | 0.012 | 0.06 | 0.012 | 0.21 | 0.835 | ||||
| Daily Internet Use | -0.027 | 0.04 | -0.032 | -0.61 | 0.542 | ||||
| Web 1.0-Based Health Information Acquisition | 0.181 | 0.04 | 0.320 | 5.19 | < 0.001** | ||||
| Web 2.0-Based Digital Health Information Acquisition | 0.078 | 0.03 | 0.150 | 2.43 | 0.016* | ||||
| Model 4 | |||||||||
| Constant | 2.687 | 0.17 | 16.16 | < 0.001** | 0.206 | 0.015 | 10.93 | < 0.001** | |
| Gender | -0.100 | 0.06 | -0.090 | -1.70 | 0.091 | ||||
| Age Group | 0.062 | 0.04 | 0.085 | 1.49 | 0.138 | ||||
| Academic Year | 0.023 | 0.06 | 0.022 | 0.38 | 0.703 | ||||
| Daily Internet Use | -0.024 | 0.04 | -0.029 | -0.55 | 0.585 | ||||
| Web 1.0-Based Health Information Acquisition | 0.143 | 0.04 | 0.252 | 3.74 | < 0.001** | ||||
| Web 2.0-Based Digital Health Information Acquisition | 0.076 | 0.03 | 0.146 | 2.37 | 0.018* | ||||
| Digital Verification | 0.071 | 0.03 | 0.142 | 2.38 | 0.018* | ||||
Durbin–Watson = 1.744 [16, 18]
Tolerance values = 0.593–0.999 [16, 18]
VIF values = 1.001–1.687 [16, 18]
*p < .05, ** p < .001
Dependent Variable: Health-Seeking Behavior
B = unstandardized coefficient; SE = standard error; β = standardized coefficient; t = t-value; R² = coefficient of determination; ΔR² = change in R²; F = F-statistic
Visual inspection of standardized residual plots indicated that the assumptions of linearity and homoscedasticity were satisfied. Furthermore, histogram and normal P–P plot examinations suggested that residuals were approximately normally distributed. Cook’s distance values were below the recommended threshold of 1.0, indicating no evidence of influential observations.
Discussion
This study examined how Web 1.0-based health information acquisition, Web 2.0-based digital health information acquisition, and digital verification behavior were associated with health-seeking behavior among university students. The findings indicate that digital health information acquisition and verification behavior were positively associated with overall health-seeking behavior. However, examination of the subdimensions revealed that these associations were not uniform across all forms of health-seeking behavior. The strongest relationships were observed for online health seeking, whereas professional health seeking showed little or no association with some dimensions of digital health information acquisition. Therefore, the findings should be interpreted with caution when referring to health-seeking behavior as a single, homogeneous construct. However, the magnitude of these associations should be interpreted cautiously, as the regression models explained a relatively modest proportion of the variance in health-seeking behavior. Although the final model explained 20.6% of the variance in health-seeking behavior, a substantial proportion of the variance remained unexplained. This finding suggests that additional factors not assessed in the present study, such as health literacy, self-efficacy, perceived health status, health anxiety, trust in healthcare professionals, prior healthcare experiences, and psychosocial characteristics, may also contribute to health-seeking behavior. Therefore, the findings should be interpreted as representing only part of a broader set of influences on health-related decision-making. Importantly, these associations remained statistically significant after adjustment for demographic characteristics, including gender, age, academic year, and daily internet use, suggesting that the observed relationships were not solely attributable to these demographic factors. This result is consistent with previous studies demonstrating a positive relationship between digital health literacy and online information-seeking behavior [1, 3, 5, 19, 20]. As highlighted by Serin et al. [21] and Kim and Oh [22], considering the high level of exposure of university students to digital environments, access to digital health information may be an important factor associated with health-related decision-making processes.
The finding that Web 1.0-based health information acquisition emerged as the strongest predictor in the hierarchical regression analysis is noteworthy. This result suggests that institutional and relatively more structured information sources may be more strongly associated with health-seeking behavior among young individuals. Montagni et al. [2] reported that university students’ trust in official health websites strengthens their information-seeking behavior. Similarly, Rantala et al. [12] emphasized that the perception of reliable sources increases online health information-seeking behavior. In this context, as indicated by Fallahi et al. [20] and Schäfer et al. [23], Web 1.0 sources explain health-seeking behavior more strongly due to their higher perceived reliability among students. Accordingly, the stronger association observed for Web 1.0 sources may partly reflect students’ perceptions of reliability and credibility. However, it should be acknowledged that contemporary digital health ecosystems increasingly integrate features of both Web 1.0 and Web 2.0 environments. Institutional health organizations frequently disseminate information through social media platforms, while user-generated content may also be informed by evidence-based sources. Therefore, the distinction between Web 1.0 and Web 2.0 should be interpreted as reflecting different patterns of information engagement rather than a simple contrast between reliable and unreliable sources. However, the correlation findings indicate that there is no significant relationship between Web 1.0-based health information acquisition and professional health seeking. This suggests that although Web 1.0 sources were positively associated with general health-seeking behavior, they were not significantly associated with professional help-seeking behavior. Taken together, these findings suggest that the association between Web 1.0-based information acquisition and overall health-seeking behavior is driven primarily by online and traditional forms of health seeking rather than by professional help-seeking behavior.
The finding that Web 2.0-based digital health information acquisition contributes significantly but at a relatively lower level to the model reflects the complex nature of the impact of social media and user-generated content on health behaviors. Frings et al. [4] noted that social media use may increase health information-seeking behavior but may also influence levels of uncertainty and anxiety. Peng et al. [8] demonstrated that unverified content may increase the tendency toward cyberchondria. In this study, the low-level negative relationship between the Web 2.0 variable and professional health seeking suggests that as individuals increasingly rely on social media-based information, their tendency to seek professional healthcare may slightly decrease. This finding is consistent with the literature indicating that digital information sources may become an alternative competing with professional healthcare services [12, 20, 21, 23].
These findings further suggest that the influence of Web 2.0 environments may depend not only on the platform itself but also on the quality, source, and verification of the information encountered. Consequently, evaluating digital health information requires attention to content characteristics rather than relying solely on distinctions between platform types. An additional consideration is that the observed association between digital health information acquisition and online health-seeking behavior may partly reflect conceptual overlap between the constructs being measured. Both variables involve engagement with online health information environments and may capture related aspects of information-seeking activity. Therefore, part of the relatively stronger association observed for the online health-seeking dimension may reflect similarities in the underlying behavioral processes assessed by the measures, rather than entirely distinct behavioral phenomena. This possibility should be considered when interpreting the magnitude of the observed relationships. An additional issue that warrants consideration is the growing use of artificial intelligence (AI)-supported tools, such as ChatGPT, Gemini, and similar conversational AI systems, by university students when seeking health-related information. Unlike traditional Web 1.0 and Web 2.0 platforms, AI-based systems can synthesize information from multiple sources and provide personalized responses in real time. While these technologies may facilitate rapid access to health information and potentially enhance digital health literacy, concerns remain regarding the accuracy, transparency, reliability, and verification of AI-generated content, particularly in health-related contexts where incorrect or misleading information may influence decision-making [24, 25]. Therefore, the increasing integration of AI tools into students’ information-seeking practices further highlights the importance of digital verification skills and critical evaluation of online health information. Future studies should investigate how AI-assisted health information acquisition influences health-seeking behavior and digital health literacy among young adults.
The incremental and statistically significant contribution of digital verification behavior to the model suggests that digital verification may be associated with health-seeking behavior and may serve as a supportive factor in health-related decision-making processes. However, the increase in explained variance attributable to digital verification was relatively small (ΔR² = 0.015), indicating that its practical contribution to health-seeking behavior should be interpreted cautiously. Although statistically significant, this finding suggests a modest rather than substantial behavioral influence. Ning et al. [19], Dadaczynski et al. [1] and Htay et al. [3], emphasize that digital health literacy is not limited to access alone but also includes the ability to critically evaluate information. The finding of a low but positive and significant relationship between digital verification and professional health seeking in the correlation analysis suggests that this variable may play a supportive role, particularly in professional help-seeking behavior. This implies that individuals who verify information may have a higher tendency to consult healthcare professionals. Therefore, digital verification appears to function as a bridge between digital information and the utilization of professional healthcare services. Nevertheless, given the relatively small incremental contribution of digital verification to the overall model, this role should be viewed as complementary rather than central in explaining health-seeking behavior among university students [19, 21]. Furthermore, the literature indicates that digital verification may represent a capacity that enhances informational support in relation to digital health literacy and may play a continuous role in health decision-making [26, 27]. From an educational perspective, these findings may also have implications for health professions education. As future healthcare professionals increasingly operate in digitally enriched healthcare environments, the ability to access, critically evaluate, verify, and appropriately utilize digital health information has become an essential professional competency. Digital health literacy skills may support evidence-informed decision-making, strengthen students’ capacity to identify misinformation, and facilitate more effective communication with patients who frequently obtain health information from online sources. Therefore, integrating digital health literacy and digital verification competencies into health-related curricula may contribute to the development of a more digitally competent healthcare workforce. It should also be noted that the scales used in this study primarily assessed self-reported tendencies and perceived behaviors rather than objectively observed health behaviors. Therefore, the findings should be interpreted as reflecting participants’ perceptions and reported practices rather than direct measures of actual health-related actions.
The present study contributes to the existing literature by examining digital health information acquisition through Web 1.0, Web 2.0, and digital verification dimensions within a single analytical framework. While previous studies have investigated digital health literacy and online information-seeking behaviors, fewer studies have compared the relative associations of these dimensions with different forms of health-seeking behavior [1, 3, 5, 19, 20]. Therefore, the study provides additional evidence regarding the multidimensional nature of digital health information engagement among university students. Future studies employing longitudinal and experimental designs may help clarify the direction and potential causal nature of the relationships observed in the present study. Moreover, comparative studies conducted across different universities and cultural contexts may provide a more comprehensive understanding of the relative effects of digital information sources.
Limitations of the study
This study was conducted using a non-probability convenience sampling method and was limited to students from a single university. In addition, the sample was predominantly composed of female students (72.5%), which may limit the representativeness of the findings across genders. Furthermore, a substantial proportion of the participants were aged 18–19 years and were first-year students, which may limit the applicability of the findings to students at different educational stages and age groups. The fact that the participants consisted solely of students enrolled at the Health Services Vocational School of Mustafa Kemal University in Hatay, Türkiye limits the generalizability of the findings to different universities and cultural contexts. Furthermore, because all participants were enrolled in health-related associate degree programs, their levels of health awareness and engagement with digital health information may differ from those of students in other academic disciplines. In addition, because the study employed a cross-sectional design, causal inferences cannot be made regarding the relationships observed between the study variables. The findings reflect associations measured at a single point in time rather than directional effects.
Furthermore, the study data were based entirely on self-reported measures. Therefore, responses may have been affected by social desirability bias and recall bias. In addition, the scales used in the study assessed perceived behaviors and self-reported tendencies rather than objectively measured health behaviors. Consequently, the findings reflect participants’ subjective evaluations of their behaviors and information-seeking practices, which may differ from their actual health-related actions. Accordingly, the findings should be interpreted as indicating associations between variables rather than causal or directional effects. In addition, the final hierarchical regression model explained only 20.6% of the variance in health-seeking behavior. Although statistically significant, this finding indicates that a substantial proportion of the variance remained unexplained. Therefore, other potentially relevant determinants, such as health literacy, self-efficacy, perceived health status, trust in healthcare professionals, health anxiety, and other psychosocial or contextual factors, were not included in the present study and may also contribute to health-seeking behavior. Future studies should examine a broader range of variables to obtain a more comprehensive understanding of the factors associated with health-seeking behavior.
Conclusion
This study demonstrated that digital health information acquisition and digital verification behavior were significantly associated with health-seeking behavior among university students. Among the examined dimensions, Web 1.0-based health information acquisition showed the strongest association with health-seeking behavior, whereas Web 2.0-based information acquisition and digital verification contributed smaller but statistically significant incremental associations. The findings further indicated that these associations were more evident for online health-seeking behavior than for professional health-seeking behavior.
Although the observed associations remained significant after adjustment for demographic characteristics, the explanatory power of the final model was modest, suggesting that additional factors may contribute to health-seeking behavior. Therefore, the findings should be interpreted as indicating meaningful associations rather than causal relationships. Future studies should examine these relationships using longitudinal designs and broader sets of psychological, behavioral, and contextual variables.
Acknowledgements
Not applicable.
Authors’ contributions
The conception, design, material preparation, data collection and analysis were carried out by AG. The first draft of the article was written and revised by AG. The author has read and approved the final version of the article.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the principles of scientific research and publication ethics. Ethical approval was obtained from the Social and Human Sciences Scientific Research and Publication Ethics Committee of Hatay Mustafa Kemal University (Decision No. 28, dated 06.02.2026). The study was conducted in accordance with the ethical standards of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.