Abstract
Background
The number of college students in need of treatment for depressive symptoms far outweighs the resources of most counseling centers due to rising health needs. It is necessary to establish prevention and intervention strategies for college students. This study aimed to explore the associations between self-efficacy, e-health literacy, and depressive symptoms to guide college students in adopting preventive measures and potential interventions for managing depressive symptoms.
Methods
This cross-sectional study randomly recruited 1500 students from two universities in Nantong City from May to December 2020. Participants’ demographic characteristics, self-efficacy, e-health literacy, and depressive symptoms were collected. The mediating effect of e-Health literacy was evaluated using the bootstrap resampling technique.
Results
A total of 1388 valid questionnaires were collected. The prevalence of depressive symptoms among the sample was 50.29%. Higher self-efficacy and e-health literacy were all significantly associated with lower depressive symptoms. e-Health literacy partially mediated the association between self-efficacy and depressive symptoms, accounting for 26.6% of the total effect.
Conclusion
Improving self-efficacy and e-health literacy was associated with reducing depressive symptoms, with e-health literacy partially mediating the relationship between self-efficacy and depressive symptoms. Interventions for college students should focus on enhancing both self-efficacy and e-health literacy to help alleviate depressive symptoms.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40359-025-03189-8.
Keywords: Self-efficacy, E-Health literacy, Depressive symptoms, College students
Introduction
In recent years, mental health has become an important topic that people pay attention to, and mental health problems, such as depressive symptoms and anxiety disorder, have become one of the health problems concerning the world. The World Health Organization (WHO)has reported that mental health problems account for approximately 15% of the global disease burden [1], with depressive symptoms being one of the most commonly reported challenges among individuals with mental health disorders [2]. Research indicates that the rates of depressive symptoms among young people have risen highly [3]. Meanwhile, mental health problems and psychological distress are more prevalent among college students than among their peers [4], which is a fact that is not surprising since college students share more vulnerabilities than the general population [1, 5]. College students represent a unique group undergoing a critical transition from adolescence to adulthood [6], placing them at higher risk for adverse mental health consequences [7]. As a result, mental health issues, especially depressive symptoms, have become an increasing concern on university campuses [8].
Research indicates that globally, about 31% of the college students have screened positive for mental health problems in recent years [9, 10], and between 25% and 50% of college students meet the criteria for at least one mental health problem each year [11, 12]. Studies among university students report that the prevalence of depressive symptoms ranged from 10 to 85%, with a weighted mean prevalence of 30.6% [13, 14]. Meanwhile, depressive symptoms are a recurring problem in college students [14] and are considered one of the most frequently reported psychiatric disorders [1]. More than 23% of Chinese college students suffer from depressive symptoms, and this rate has been increasing over the past decade [13]. Moreover, depressive symptoms are now the leading cause of disability in China [15]. Given the high prevalence of depressive symptoms in students, it is crucial to pay more attention to this group, necessitating practical strategies for assessment, prevention, and treatment [16]. However, more than two-thirds of college students do not seek help for depressive symptoms at college counseling centers [17], and only 1 in 5 students with depressive symptoms receive adequate treatment [11, 18]. With increasing health needs and shifting attitudes towards mental health, the demand for mental health services has risen dramatically in recent years [10]. As the number of students requiring treatment for depressive symptoms far exceeds the resources available at most counseling centers, there is a significant unmet need for treatment of depressive symptoms among college students [11].
An increase in the number of students seeking mental health services [10] and the neglect of early disease means that an accessible, convenient, and cheap approach needs to be taken to support health intervention and prevention. Research consistently indicates that Internet-based interventions have the potential to improve access to mental health treatment while overcoming many existing barriers associated with traditional face-to-face services [19]. Internet-based interventions may also apply to college students [20], and studies have shown that seeking help through the Internet is more prevalent among younger and well-educated individuals [21]. In addition, studies on technology-enabled mental health interventions among college students demonstrate that Internet-based interventions can effectively reduce depressive symptoms [22]. Internet-based interventions may be an acceptable, practical, and potentially cost-effective way to reduce the negative impact of depressive symptoms associated with college students [23].
Further, previous research on relevant interventions focused exclusively on randomized controlled trials (RCTS) [36], and the inclusion criteria were patients with established depressive symptoms. Other studies indicate that there are still many medical student college students with low health literacy who neglect mental health [24]. They are at high risk of developing depressive symptoms [25], or they may already be experiencing varying degrees of depressive symptoms. Moreover, some college students may perceive their symptoms as typical stress associated with college life [24], leading them to believe their health is not at risk. Studies have also confirmed that college students with medical backgrounds have better access to all aspects of health knowledge and better health literacy than non-medical college students, resulting in a lower prevalence of depressive symptoms [26]. In conclusion, the depressive symptoms challenges faced by college students call for effective interventions and guides for depressive symptoms [27]. In China, the campus is the primary place for college students to study and live, and almost all college students must live in dormitories and spend most of their time on campus [28]. It is crucial to reduce the incidence, prevalence, severity, duration, and consequences of depressive symptoms on campus [29]. Reviews of various surveys have emphasized the significance of promoting healthy internet use on campus [30], which must be effectively and sustainably integrated into available mental health services [31]. Therefore, this study focused on college students as the research population, combined with the use of the Internet, and tried to explore the action path that can prevent depressive symptoms in the early stage or more severe and promote healthy lifestyle choices. It will also help guide college students in adopting preventive measures and potential interventions to manage depressive symptoms [32].
The relationship between self-efficacy and depressive symptoms
Previous research has identified that Chinese college students have a high demand for mental health guidance, such as depressive symptoms. However, some students with limited mental health literacy may overlook these issues, making it challenging to provide comprehensive care for depressive symptoms [31]. Therefore, improving their abilities in health intervention and health behaviors may be a convenient and effective means of addressing depressive symptoms.
According to the Self Determination Theory (SDT), motivation for healthy behavior is influenced by healthy autonomy and consistent confidence, which are crucial for initiating and maintaining healthy behaviors over time [33]. Furthermore, Self-efficacy theory reports that self-efficacy influences people’s decision to engage in a behavioral task and their perseverance and level of effort [34]. Its role reflects how people perceive their abilities and attitudes, affecting how people think, act, and feel while performing tasks [35]. According to the Health Belief Model (HBM) [36] and Theory of Planned Behavior (TPB) [37], self-efficacy can change the intention to implement healthy behaviors by improving the belief in overcoming disease, which is the same as Social Cognitive Theory (SCT) [38], self-efficacy affects the individual’s judgment of self-ability and the degree of control over intervention behavior in the implementation of health behaviors [39]. Consequently, people with high self-efficacy are more likely to engage in health intervention programs and exert greater efforts to improve depressive symptoms [40]. Based on this framework, we propose the first hypothesis: Self-efficacy is negatively associated with depressive symptoms.
The relationship between e-health literacy and depressive symptoms
With the rapid development of electronic products and the Internet, global usage has steadily increased, among which college students, the leading Internet users, have also been widely concerned [41]. e-Health literacy, as the level of health literacy in Internet use, was defined by Norman and Skinner as the ability to use the web to search for, understand, and utilize health-related knowledge to promote one’s health [42, 43]. Meanwhile, the Internet has become one of the sources available to college students for health information and knowledge [44]. It is a potent source of health-related information and a powerful platform for changing people’s health behaviors and lifestyles and handling health-related queries [45]. Thus, the promptness and efficiency of acquiring health-related information using the Internet was particularly crucial. Studies on e-health literacy showed that people with high e-health literacy are more inclined to acquire health-related knowledge and adopt health behaviors to keep physically and mentally fit. They also showed higher adherence to physical and mental health-related behaviors than those with low e-health literacy [46]. Growing evidence indicates that e-health literacy reduces the risk of depressive symptoms [47]. Based on the analysis, we propose the second hypothesis: e-Health literacy is negatively associated with depressive symptoms.
The relationship between e-health literacy, self-efficacy, and depressive symptoms
According to the Integrative Model of e-Health Use (IMeHU) [48], Whether the Internet and social media can improve health outcomes depends on users’ intention and behavior in seeking health information. Self-efficacy, a key determinant of behavior, influences the beliefs and attitudes that drive the initiation and execution of actions [49]. Previous studies have confirmed that e-health literacy is strongly associated with mental health and healthy behavior [50]. Information Motivation Behavioral Skills Model (IMB) [51, 52] proposes that, in addition to the need for sufficient motivation for healthy behaviors, individuals must have the skills to improve healthy behaviors in life [53]. Studies show that although the self-efficacy and motivation of students largely determine healthy action, the development and application of technology have influenced the behavioral execution processes and students’ learning abilities [54]. Therefore, we propose the third hypothesis: H3. Self-efficacy affects depressive symptoms through the mediating effects of e-health literacy.
Methods
Sampling and study design
In this study, based on the geographical locations and regional representativeness, the students of two universities—one with a medical major and one without— were randomly selected in Nantong City from May to December 2020 using a random number table. After obtaining approval from the relevant school authorities, this study randomly recruited college students to participate in the questionnaire survey (Appendix 1). The criteria for excluding invalid questionnaires were (1) apparent regularity in responses and an excessive number of neutral options (e.g., all “undecided” selections on eHEALS items); (2) a completion time of less than 2 min (as the pre-survey analysis showed an average completion time of 4.38 min, SD = 0.54); and (3) there were apparent errors (e.g., errors in “Grade” or “Major”). Additionally, we obtained informed consent from all participants before the questionnaire survey.
The sample size was calculated using the Raosoft online sample size calculator with a confidence interval of 95% and a margin of error of 3%. The prevalence of e-Health literacy was estimated at 50%. The number of students in the two universities was nearly 80,000, and the calculated sample size was 1054. Six or seven classes were randomly selected from each university, and the university identified potentially eligible participants in these classes. The inclusion criteria were as follows: (1) current enrollment in this university, (2) ability to understand questionnaires, and (3) willingness to be interviewed. The exclusion criteria were mental disorders and withdrawal from the survey process. 1500 college students were recruited from five different grades, including the five years of medical school in China, for the survey. 1404 (response rate was 93.6%) questionnaires by paper and pencil were received, and participants who finished the paper were issued a doll. After eliminating all the unfinished surveys and invalid responses, a total of 1388 valid questionnaires were collected.
Measurements
Depressive symptoms
Widely used Zung’s Self-rating Depression Scale (SDS) was used to assess depressive symptoms [55]. It contains 20 items designed to measure an individual’s depressive symptoms and their severity in the past week. Each item is rated on a four-point scale (1 = a little of the time and 4 = most of the time) according to the frequency of symptoms occurrence, and total scores range from 20 to 80. The scale was divided into four categories [56]: 1) pervasive affective disturbances (items 1 and 3), (2) physiological disturbances (items 2 and 4 to 10), (3) psychomotor disturbances (items 12 and 13), and (4) psychological disturbances (items 11 and 14 to 20). In China, SDS raw scores lower than 50 indicate no depressive symptoms, while scores above 70 indicate severe depressive symptoms [57, 58], and it has been widely used among Chinese college students [59]. The Cronbach α coefficient for the scale in this study was 0.82.
Self-efficacy
The General Self-Efficacy Scale (GSES) was developed by Schwarzer et al. to assess self-efficacy [60]. The Chinese version of the scale was reliable and valid and has been widely used [61], and a large number of studies have also confirmed the application of this scale among Chinese college students [62, 63]. 10 items in the GSES assess an individual’s confidence in facing setbacks or difficulties. It is a 4-point Likert scale, with each item scored from 1 to 4 (1 = not at all true; 4 = intensely accurate), and the summed total score ranges from 10 to 40. It is a single-dimensional scale with no subscales, so points are calculated by adding the scores for all 10 items. The Cronbach α coefficient for the scale was 0.89 in this study.
e-health literacy
The e-Health Literacy Scale (eHEALS) was developed by Norman and Skinner and was initially used with adolescents in Canada [64]. The eHEALS is an 8-item measure of e-health literacy that mainly evaluates the self-perception skills of Internet users in seeking and applying Internet health knowledge to solve health problems [42]. The Chinese version was translated and verified by Guo et al. [65]; the scale items have a 5-point Likert scale rating method: “strongly disagree = 1, disagree = 2, undecided = 3, agree = 4, strongly agree = 5”. A score of 8 is the lowest, and 40 is the highest. Higher scores on the scale indicate higher levels of e-health literacy [66]. The Chinese version scale has been used among college students [67]. The Cronbach’s alpha value was 0.87 in our study.
Other variables
Based on the existing research, this study included college students of different grades to assess the longitudinal impact on their health status [68, 69]. To evaluate the role of health-related knowledge, we also compared students from medical and non-medical majors [70]. We also included factors such as birthplace, parental educational level, and family economic status to examine the influence of family and living environment on health outcomes [71]. In addition, self-reported health indicators were included to assess perceived health status [72]. Other variables were defined as follows: gender (Male = 0 and Female = 1), grade (First-year = 1, Second-year = 2, Third-year = 3, Fourth-year = 4, Fifth-year = 5(the five years of medical university in China)), major (Medical = 0 and non-Medical = 1), academic (Good = 1, Fair = 2, Poor = 3), birthplace (Urban = 0, Rural = 1), parents’ education (Primary school and below = 1, Junior high school = 2, High school = 3, Above high school = 4), family economic status (Good = 1, Fair = 2, Poor = 3), and self-rated health (Good = 1, Fair = 2, Poor = 3).
Statistical analysis
Statistics were calculated using STATA 18.0 and Mplus 8.3. The categorical variables were described using numbers and percentages, while continuous data were presented as mean ± standard deviation (S.D.). The mediation model was constructed using Structural Equation Modeling (SEM). Model 1 examined the association between self-efficacy and depressive symptoms. Model 2 examined the relationship between self-efficacy and e-health literacy, with e-health literacy as the dependent variable. Model 3 examined the association between depressive symptoms and e-health literacy. In Model 4, e-health literacy and self-efficacy were the independent variables, while depressive symptoms served as the dependent variable, analyzing how self-efficacy affects depressive symptoms through the mediating role of e-health literacy. The bootstrap resampling technique was used to evaluate the mediating effect.
Results
Participant characteristics
Table 1 summarizes the participants’ sociodemographic characteristics. A total of 1388 college students were evaluated, of which 477 (34.37%) were male and 911 (65.63%) were female. The participants were from 5 different grades, and 25.57% self-reported having a good academic record; 50.58% had received a variety of health-related education. Regarding the place of birth, 49.86% were born in rural areas. Regarding parents’ educational level, about half of the participants’ fathers and mothers were below junior high school. Most students (69.73%) reported their family’s economy was fair. The students reported their health as good, fair, and poor were 51.01%, 43.88% and 5.06%, respectively.
Table 1.
Characteristics of college students
| Variables | n(%) | e-Health literacy | Depressive symptoms | ||
|---|---|---|---|---|---|
| Mean ± SD | P | Mean ± SD | P | ||
| Gender | 0.473 | 0.219 | |||
| Male | 477(34.37) | 29.68 ± 6.98 | 40.29 ± 9.14 | ||
| Female | 911(65.63) | 29.43 ± 5.65 | 39.69 ± 8.42 | ||
| Grade | 0.890 | < 0.001 | |||
| First-year | 545(39.27) | 29.40 ± 6.43 | 41.68 ± 8.37 | ||
| Second-year | 401(28.89) | 29.43 ± 5.87 | 40.13 ± 8.61 | ||
| Third-year | 283(20.39) | 29.71 ± 6.32 | 37.05 ± 8.88 | ||
| Fourth-year | 119(8.57) | 29.61 ± 5.26 | 38.34 ± 8.00 | ||
| Fifth-year | 40(2.88) | 30.23 ± 5.91 | 38.00 ± 7.86 | ||
| Major | 0.139 | < 0.001 | |||
| Medicine | 663(47.77) | 29.78 ± 5.69 | 37.93 ± 8.75 | ||
| Non-medicine | 725(52.23) | 29.28 ± 6.51 | 41.69 ± 8.21 | ||
| Academic Record | 0.002 | < 0.001 | |||
| Good | 355(25.57) | 30.40 ± 6.61 | 38.32 ± 8.90 | ||
| Fair | 718(51.73) | 29.41 ± 5.84 | 39.94 ± 8.53 | ||
| Poor | 315(22.70) | 28.74 ± 6.12 | 41.57 ± 8.45 | ||
| Health Education | < 0.001 | < 0.001 | |||
| Yes | 702(50.58) | 30.23 ± 6.37 | 39.00 ± 8.92 | ||
| No | 686(49.42) | 28.78 ± 5.80 | 40.81 ± 8.33 | ||
| Birthplace | 0.056 | 0.031 | |||
| Urban | 696(50.14) | 29.82 ± 6.39 | 39.30 ± 8.98 | ||
| Rural | 692(49.86) | 29.20 ± 5.85 | 40.49 ± 8.32 | ||
| Father’s Education | 0.007 | < 0.001 | |||
| Primary school and below | 230(16.57) | 28.52 ± 6.11 | 42.24 ± 7.85 | ||
| Junior high school | 501(36.10) | 29.35 ± 5.88 | 39.63 ± 8.27 | ||
| High school | 370(26.66) | 29.69 ± 6.11 | 39.24 ± 8.81 | ||
| Above high school | 287(20.68) | 30.36 ± 6.52 | 39.33 ± 9.52 | ||
| Mother’s Education | < 0.001 | < 0.001 | |||
| Primary school and below | 342(24.64) | 28.23 ± 6.10 | 41.72 ± 8.02 | ||
| Junior high school | 498(35.88) | 29.90 ± 5.87 | 38.85 ± 8.48 | ||
| High school | 318(22.91) | 29.67 ± 6.04 | 40.02 ± 8.66 | ||
| Above high school | 230(16.57) | 30.37 ± 6.61 | 39.28 ± 9.62 | ||
| Family Economic Status | 0.004 | < 0.001 | |||
| Good | 156(11.24) | 30.46 ± 6.98 | 39.56 ± 9.58 | ||
| Fair | 954(68.73) | 29.64 ± 5.92 | 39.31 ± 8.58 | ||
| Poor | 278(20.03) | 28.54 ± 6.27 | 42.09 ± 8.12 | ||
| Self-rated Health | < 0.001 | < 0.001 | |||
| Good | 708(51.01) | 30.12 ± 6.32 | 38.52 ± 8.55 | ||
| Fair | 609(43.88) | 28.80 ± 5.77 | 40.94 ± 8.54 | ||
| Poor | 71(5.06) | 29.52 ± 6.66 | 44.73 ± 8.31 | ||
| Depressive Symptoms | < 0.001 | ||||
| No | 690(49.71) | 1.46 ± 0.56 | |||
| Mild | 348(25.07) | 1.58 ± 0.61 | |||
| Moderate | 328(23.63) | 1.62 ± 0.59 | |||
| Severe | 22(1.59) | 2.18 ± 0.66 | |||
Factors associated with four dimensions of depressive symptoms
The four dimensions of depressive symptoms were analyzed to determine the association between self-efficacy and e-health literacy. In Dimension 1, second and third-year participants were more likely to experience pervasive affective disturbances compared to first-year participants. A higher educational level of mothers was associated with fewer pervasive affective disturbances. In Dimension 2, those who rated their academic record as “poor” were more likely to experience physiological disturbances, while those who reported their family economic status as “fair” or “poor” (compared to “good”) were less likely to experience such disturbances. In Dimension 3, those with health-related education reported fewer psychomotor disturbances. In Dimension 4, the scores of e-health literacy and self-efficacy were negatively associated with psychological disturbances. Additionally, across Dimensions 1–4, better Self-rated Health (SRH) was consistently negatively associated with the four subscale scores of depressive symptoms (Table 2).
Table 2.
Association between the four dimensions of depressive symptoms, self-efficacy, and e-health literacy
| Variables | Pervasive affective disturbances | Physiological disturbances | Psychomotor disturbances | Psychological disturbances | ||||
|---|---|---|---|---|---|---|---|---|
| β | SE | β | SE | β | SE | β | SE | |
| Self-efficacy | -0.012 | 0.007 | -0.047* | 0.018 | -0.028*** | 0.007 | -0. 166*** | 0.026 |
| e-Health Literacy | -0.044*** | 0.007 | -0.105*** | 0.019 | -0.378*** | 0.007 | -0. 191*** | 0.025 |
| Grade | ||||||||
| First-year | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Second-year | 0.274* | 0.135 | 0.205 | 0.346 | -0.207 | 0.135 | 0.352 | 0.483 |
| Third-year | 0.398* | 0.192 | -0.140 | 0.491 | -0.465* | 0.191 | -1.359 | 0.686 |
| Fourth-year | 0.114 | 0.218 | 0.034 | 0.558 | -0.298 | 0.217 | -0.702 | 0.779 |
| Fifth-year | -0.084 | 0.299 | -0.021 | 0.764 | -0.204 | 0.297 | -0.203 | 1.068 |
| Medicine | ||||||||
| No | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Yes | -0.314 | 0.161 | -2.045*** | 0.411 | -0.362* | 0.160 | -0.793 | 0.574 |
| Academic Record | ||||||||
| Good | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Fair | -0.103 | 0.103 | 0.395 | 0.264 | 0.105 | 0.103 | 0.410 | 0.369 |
| Poor | -0.030 | 0.125 | 0.705* | 0.320 | 0.200 | 0.124 | 1.230** | 0.447 |
| Health Education | ||||||||
| Yes | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| No | 0.039 | 0.086 | -0.063 | 0.220 | 0.092 | 0.086 | 0.107 | 0.308 |
| Birthplace | ||||||||
| Urban | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Rural | -0.069 | 0.099 | 0.260 | 0.253 | 0.063 | 0.098 | -0.148 | 0.353 |
| Fathers’ Education | ||||||||
| primary school and below | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Junior high school | -0.018 | 0.138 | -0.396 | 0.353 | -0.227 | 0.137 | -0.749 | 0.494 |
| High school | -0.292 | 0.159 | -0.485 | 0.407 | -0.267 | 0.159 | -0.973 | 0.569 |
| Above high school | 0.044 | 0.190 | -0.475 | 0.488 | -0.271 | 0.190 | -0.816 | 0.681 |
| Mothers’ Education | ||||||||
| primary school and below | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Junior high school | -0.390* | 0.192 | -0.364 | 0.315 | -0.063 | 0.123 | -0.492 | 0.440 |
| High school | -0.493* | 0.195 | 0.004 | 0.387 | 0.196 | 0.150 | 0.347 | 0.540 |
| Above high school | -0.551* | 0.258 | 0.443 | 0.473 | 0.142 | 0.184 | 0.303 | 0.660 |
| Family Economic Status | ||||||||
| Good | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Fair | -0.184 | 0.143 | -1.385*** | 0.364 | -0.131 | 0.141 | -0.684 | 0.508 |
| Poor | 0.100 | 0.171 | -1.249** | 0.438 | 0.077 | 0.170 | -0.339 | 0.612 |
| Self-rated Health | ||||||||
| Good | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Fair | 0.532*** | 0.091 | 1.159*** | 0.232 | 0.259** | 0.090 | 1.411*** | 0.324 |
| Poor | 1.146*** | 0.200 | 3.168*** | 0.512 | 0.515** | 0.199 | 2.716*** | 0.715 |
| Adj R 2 | 13.18% | 14.76% | 12.59% | 16.34% | ||||
NOTE: * P < 0.05, ** P < 0.01, *** P < 0.001
Effects of self-efficacy and e-health literacy on depressive symptoms
Table 3 shows the estimates of Models 1 to 4 after controlling for confounding factors. In Model 1, self-efficacy showed a negative coefficient, indicating that self-efficacy is negatively associated with depressive symptoms. Non-medical students were more vulnerable to depressive symptoms than their medical counterparts. Those with “poor” academic records were also at higher risk of experiencing depressive symptoms compared to other students. Compared to the others, students with good self-rated health had fewer depressive symptoms. In Model 2, those without any health-related education had lower e-health literacy. Models 3 and 4 showed that e-health literacy was negatively associated with depressive symptoms, and third-year students showed higher depressive scores. After controlling for all covariates, Model 4 accounted for 21.09% of the variance in depressive symptoms (Table 4).
Table 3.
Association between e-health literacy, self-efficacy, and depressive symptoms
| Variables | Model 1 a | Model 2 b | Model 3 a | Model 4 a | ||||
|---|---|---|---|---|---|---|---|---|
| β | SE | β | SE | β | SE | β | SE | |
| Self-efficacy | -0.303*** | 0.035 | 0.286*** | 0.026 | -0.282*** | 0.036 | ||
| e-Health Literacy | -0.347*** | 0.035 | -0.222*** | 0.036 | ||||
| Grade | ||||||||
| First-year | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Second-year | 0. 163 | 0. 694 | -0.362 | 0.505 | 0.046 | 0.688 | 0.061 | 0.679 |
| Third-year | 1.792 | 0.985 | -0.383 | 0.718 | -1.928* | 0.978 | 1.890* | 0.965 |
| Fourth-year | -0.795 | 1.119 | -0.236 | 0.815 | -0.777 | 1.110 | -0.862 | 1.095 |
| Fifth-year | -0.432 | 1.534 | 0.078 | 1.117 | -0.377 | 1.521 | -0.410 | 1.502 |
| Medicine | ||||||||
| No | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
| Yes | -2.873** | 0.825 | 0.222 | 0.601 | -2.883*** | 0.818 | -2.811** | 0.808 |
| Academic Record | ||||||||
| Good | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Fair | 0.739 | 0.530 | -0.330 | 0.386 | 1.059 | 0.845 | 0.646 | 0.519 |
| Poor | 1.825** | 0.641 | -0.497 | 0.467 | 2.152** | 0.632 | 1.685** | 0.628 |
| Health Education | ||||||||
| Yes | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| No | 0. 403 | 0.440 | -0.935** | 0.321 | 0.270 | 0.438 | 0.139 | 0.432 |
| Birthplace | ||||||||
| Urban | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Rural | 0.432 | 0.507 | -0.046 | 0.369 | 0.160 | 0.503 | 0.084 | 0.496 |
| Fathers’ Education | ||||||||
| primary school and below | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Junior high school | -1.085 | 0.709 | -0.093 | 0.516 | -1.130 | 0.703 | -1.111 | 0.694 |
| High school | -1.640* | 0.817 | 0.095 | 0.596 | -1.630* | 0.811 | -1.614* | 0.800 |
| Above high school | -1.322 | 0.977 | 0.378 | 0.714 | -1.233 | 0.970 | -1.215 | 0.957 |
| Mothers’ Education | ||||||||
| primary school and below | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Junior high school | -1.562* | 0.630 | 1.319** | 0.459 | -0.967 | 0.627 | -0.890 | 0.619 |
| High school | 0.156 | 0.77 | 1.030 | 0.565 | 0.455 | 0.770 | 0.417 | 0.760 |
| Above high school | 0.391 | 0.947 | 1.030 | 0.690 | 0.489 | 0.941 | 0.622 | 0.928 |
| Family Economic Status | ||||||||
| Good | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Fair | -2.014** | 0.729 | 0.278 | 0.531 | -1.718* | 0.723 | -1.936** | 0.714 |
| Poor | -1.081 | 0.878 | -0.255 | 0.640 | -0.884 | 0.870 | -1.153 | 0.860 |
| Self-rated Health | ||||||||
| Good | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| Fair | 2.947*** | 0.464 | -0.916** | 0.338 | 2.886*** | 0.461 | 2.689*** | 0.456 |
| Poor | 6.061*** | 1.027 | -0.089 | 0.748 | 6.081*** | 1.019 | 6.036*** | 1.006 |
| Adj R 2 | 17.62% | 12.59% | 18.89% | 21.09% | ||||
NOTE: a : dependent variable = depressive symptoms; b: dependent variable = e-health literacy; * P < 0.05, ** P < 0.01, *** P < 0.001
Table 4.
Models of the mediating role of e-health literacy in the relationship between self-efficacy and depressive symptoms
| Effect | β | S.E. | Z | P | 95% CI |
|---|---|---|---|---|---|
| Total effect (c) | -0.303 | 0.035 | -8.60 | < 0.001 | (-0.372, -0.234) |
| Direct effect (c’) | -0.222 | 0.036 | -6.18 | < 0.001 | (-0.305, -0.139) |
| Indirect effect (α*b = c-c’) | -0.081 | 0.013 | -6.36 | < 0.001 | (-0.105, -0.056) |
Figure 1 shows the results of the mediation effect of e-health literacy. E-health literacy had a mediation effect on the relationship between self-efficacy and depressive symptoms. Path c presents the total effect on self-efficacy and depressive symptoms. Path c’ shows the association between self-efficacy and depressive symptoms, incorporating the effects of e-health literacy. All paths in the model were statistically significant, verifying all the proposed hypotheses.
Fig. 1.
The mediating effect of e-health literacy on the relationship between self-efficacy and depressive symptoms. NOTE: c: total effect, c’: direct effect, ɑ:the effect of self-efficacy on e-health literacy, b: the effect of e-health literacy on depressive symptoms, * P < 0.05, ** P < 0.01, *** P < 0.001
Table 4 presents the effects of the mediation analysis. Our results showed that the association between self-efficacy and depressive symptoms was partially mediated by e-health literacy. The direct, indirect, and total effects of self-efficacy on depressive symptoms were significant. The indirect effect of e-health literacy on depressive symptoms represented 26.6% of the total effect.
Discussion
In our survey, the descriptive statistics showed the prevalence of depressive symptoms was 50.29%, with mild depressive symptoms at 25.07%, moderate depressive symptoms at 23.63%, and severe depressive symptoms at 1.59%. This incidence was higher than the 27.2% prevalence reported in a previous systematic review and meta-analysis of medical students in China [73]. It is worth noting that our study included both medical and non-medical students, and medical students have higher access to health-related information and services during their university than non-medical college students. Several studies in China reported that the prevalence of depressive symptoms among college students ranged from 13.5–68.5% [73–75], which may be influenced by factors such as different regions and environments. This data can also reflect that the rate of students seeking mental health services is relatively low. This is closely related to college students’ awareness of depressive symptoms and their willingness to seek help [76]. Studies have found that incorrect cognition and the sense of shame in treatment may increase the risk of depression in the future [77, 78]. Given the relatively high prevalence of depressive symptoms among college students, it is crucial to explore more accessible, efficient, and effective approaches to prevent and reduce depressive symptoms.
Our study explored the in-depth association between four dimensions of depressive symptoms, self-efficacy, and e-health literacy in college students. We found that urban students were negatively associated with pervasive affective disturbances, and this finding is consistent with similar studies [60]. A higher risk of pervasive affective disturbances was observed among students whose mothers had a lower educational level, aligning with other studies’ findings [79]. The relationship-focused theory suggests that parental involvement plays a crucial role in addressing young people’s socio-emotional and developmental needs [80]. At times, the mother’s educational level might affect the understanding of the relationship between the father’s academic level and the healthy development of children. Hence, the mother’s educational level became one of the independent socioeconomic predictors of child health inequalities [81]. We found that the family’s economic status was only associated with the physiological disturbances of depressive symptoms, which is similar to other studies on college students [82]. We speculated that students with high financial burdens would pay attention to physical health to reduce economic losses, and they would choose part-time jobs to earn money in their spare time to reduce the financial burden on their families. It is noteworthy that being in the third year, compared to other academic years, was associated with higher pervasive affective disturbances, psychomotor disturbances, and psychological symptoms. It is inconsistent with a longitudinal study from China, which explored gender-based differences in depressive symptoms and showed that, for all genders, the prevalence of depressive symptoms decreased over time during the junior year [83]. We speculate that it may have been affected by a significant public health event, such as the COVID-19 pandemic, which likely exacerbated depressive symptoms among students. Additionally, the influence of changing students’ employment or internship pressure over time cannot be ruled out.
Further, our participants’ self-rated health was negatively associated with various dimensions of depressive symptoms. This finding aligns with other studies suggesting that self-rated health can predict long-term outcomes of depressive symptoms [84] and that higher levels of stress and depressive symptoms may contribute to poorer self-rated health [85]. Further studies should focus more on the different dimensions of depressive symptoms among college students, and more comprehensive methods should be proposed to improve college students’ depressive symptoms according to different paths and aspects.
This study explored the pathway relationship between self-efficacy, e-health literacy, and depressive symptoms, and the results showed the mediating role of e-health literacy in the relationship between self-efficacy and depressive symptoms in college students. College students with low self-efficacy reported more depressive symptoms, a finding consistent with previous research [86]. Furthermore, we found that e-health literacy was negatively associated with depressive symptoms, suggesting that lower e-health literacy may be a risk factor for depressive symptoms. Prior research has also demonstrated a significant association between depressive symptoms and e-health literacy [46, 64], and our findings are consistent with these studies. As mentioned, improving e-health literacy among college students could be an effective strategy to reduce depressive symptoms. College students, who represent the young population, are also active users of the Internet and social networking sites, and they skillfully use these channels to obtain the health-related information they need [87]. The cognitive level of college students can obtain and use health information in multiple ways, as well as the sense of psychological self-esteem and sensitivity, which accelerates their use of online resources to obtain health information. They tend to trust this online health-related information highly [88]. In addition, based on IMeHU and IMB, we also explored whether e-health literacy mediates the relationship between self-efficacy and depressive symptoms. The results showed e-health literacy was a partial mediator, and the mediating role accounted for 26.6% of the total mediating role. Self-efficacy, as one of the core driving forces for individuals to change their health, plays a guiding role in the change of health behaviors [89, 90]. This can also explain the partial mediating role of e-health literacy in depressive symptoms in this research approach. Simultaneously, as a predictor of motivation and achievement behavior, self-efficacy can construct students’ cognition, skill learning, and performance in different actions to promote their health behavior and access to health-related information [35]. Therefore, by enhancing self-efficacy, universities can improve depressive symptoms in college students by boosting their e-health literacy.
Our study has several limitations. Firstly, it was a cross-sectional investigation that showed no causal relationship between self-efficacy, e-health literacy, and depressive symptoms. Additionally, our study participants were mainly college students in Nantong City, meaning the sample may not be representative of all college students in China. Future research should aim to include a more diverse sample from different regions across the country.
Conclusions
Based on the mediation analysis, this study included relevant factors that may influence the outcome variables through a multidimensional approach. The results indicate that e-health literacy is a statistically significant mediating variable. Therefore, improving e-health literacy could help alleviate depressive symptoms among college students, especially in the context of the rapid development of the Internet. Based on the above findings, universities could enhance students’ self-efficacy and promote e-health literacy by offering relevant courses. This would enable students to make better use of online health information, ultimately improving their overall health and well-being.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We are particularly grateful to the support received from all participants in this study.
Author contributions
Yaqin Zhong and Yanan Wang conducted the study and held the main responsibility for writing the manuscript. Zhuoya Yang and Wenkun Xu conducted the statistical analysis. Bingda Yan, and Miaomiao Zhao drafted parts of the paper and revised the manuscript. Rujian Lu revised the manuscript. All authors have read and approved the manuscript.
Funding
This work was supported and funded by “Prioritized Key Project of the 14th Five-Year Plan of Education and Science in Jiangsu Province (Grant number: B/2021/01/74)” and “National Natural Science Foundation of China (Grant number: 72004104)”.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Ethical approval was received from the Institutional Review Boards at the University of Nantong. All participants gave informed consent before taking part in the study. This study was conducted following the relevant guidelines and regulations.
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.
Yaqin Zhong and Yanan Wang contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.