Public, professionals, students, and patients have become active users of Internet information,1-3 which might develop cyberchondria and Internet addiction and their related anxieties. The Internet is rife with inaccurate information;4 thus, such online information exposes users to severe risks about their perceived conditions.2, 5 Access to online medical information exacerbates fear, anxiety about health, or obsessive-compulsive behaviors.2, 3, 6-9
Both cyberchondria and Internet addiction are relatively newly emerging issues.1, 3, 10-12 Cyberchondria, the excessive referral to online websites to satisfy medical curiosity,1, 2, 4, 8, 9, 13-15 has associations with elevated anxiety about one’s health.1-4, 15-17 It has bidirectional relationships with many other factors. For instance, cyberchondria is associated with self-misdiagnosis and exaggerated health anxiety.1-3, 10, 12, 16, 17 and is predicted by many factors such as Internet addiction, anxiety sensitivity, and health anxiety.2, 8, 9, 18-20
Additionally, health anxiety has a dual effect on cyberchondria and Internet addiction. It was known as a potential risk factor for cyberchondria and, in turn, Internet addiction.3, 4, 7, 9 Internet addiction is a behavioral addiction marked by a compulsive desire to do repetitively online activities such as browsing and gaming.10, 12, 17
Terminological differences exist between studies about problematic Internet use, including cyberchondria and Internet addiction.1, 3, 4, 7, 9, 10, 12, 14 For the current study, the terms “Internet addiction” and “cyberchondria” were used only, as they are the closest to terms used to describe the phenomena in Jordan. “Internet addiction” has different levels expected to escalate from mild to moderate levels to severe ones. This increase is a result of the growth of information technology4 and contagious diseases such as the COVID-19.4
Studies have reported a relationship between viral diseases such as COVID-19 and anxiety problems.16, 21 People who have a high level of anxiety sensitivity may believe their feelings and symptoms are dangerous. People worry excessively about their health as a result of what they read on the Internet. As a result, Internet addiction during COVID-19 has been assessed heavily, including many studies discussing the relationship between cyberchondria and internet addiction.16, 21-26 Similarly, the literature also links cyberchondria to Internet-related behavior addiction.27, 28
Although the Internet is helpful, yet it causes many problems such as abuse symptoms and Internet addiction.29 Internet addiction has become a problematic issue with the increased and widespread use of smartphones. There is an increase in students’ “Internet on-the-go” phenomenon, allowing for unlimited data usage and using the Internet at home or on the go.
Historically, infectious diseases invoked fear; however, it has never been as the fear associated with the COVID-19 pandemic could be related to global Internet connectivity.4 Internet has become an essential source of health information; rapid communication took place using social media platforms.4 This per se initiated cyberchondria and resulted at a later stage in psychological problems such as Internet addiction. COVID-19 anxiety and feelings of insecurity trigger a compulsive online search for health information, causing an aggressive and nonstoppable cycle of cyberchondria,4 and in turn, resulting in Internet addiction. In consequence, the World Health Organization (WHO) (2020)30 declared that the world is facing two major health threats; “a pandemic and an infodemic.” Cyberchondria and Internet addiction levels among students are expected to elevate in future studies to satisfy medical curiosity and to meet the requirements of different courses.1, 3, 4, 8, 9, 13-15, 17 Before COVID-19, cyberchondria and Internet addiction levels were mild to moderate,1, 3, 4, 7, 9, 10, 12, 14 but after COVID-19 were subjected to severe levels.4
Research on cyberchondria and Internet addiction was little, focusing on the individualized notions.1-4, 10, 12, 16, 20, 31, 32 Few studies were conducted about cyberchondria, and the variables of the risk factors for cyberchondria were: Internet addiction,8, 9, 19, 20 anxiety sensitivity and health anxiety,2, 8, 19 and problematic Internet use.2, 18, 19 There is little published data on students regarding the studied mentioned concepts.17 In recent studies, COVID-19 guided the scene, students’ fear of COVID-19 associated with Internet addiction, anxiety, and depression.17 As of this writing, this study is the first study about cyberchondria and Internet addiction together on the national level. In addition, there are few predictors-related studies about cyberchondria, health sensitivity, and health anxiety;33-36 none were about predictors of Internet addiction.
The current study addresses the following questions: (1) What are cyberchondria and Internet addiction levels in a sample of Jordanian students, based on total scores? (2) What is the relationship between cyberchondria and Internet addiction in a sample of Jordanian students? (3) Does cyberchondria (independent variable) among students predict their Internet addiction (dependent variable) during the COVID-19 pandemic while controlling students’ characteristics (i.e., age, gender, years of using the Internet (years online), income, availability of Internet at school, level of education, academic level (senior vs. junior) and stream, grade point average (GPA), and whether they had infected with COVID-19 or not)? Based on the current study’s findings, various interventions could be established and implemented in the academic settings to manage students’ issues with cyberchondria, Internet addiction, and different types of anxieties.
2 MATERIALS AND METHODS
This study is an online survey using a cross-sectional research design and convenience snowball sampling of university students.
2.2 Sample and data collection and ethics
In April 2021, data were collected through convenience snowball sampling of students enrolled in e-learning courses. By combining the snowball method with convenience sampling, the researchers could find potential participants who might have information relevant to the study. The study was conducted at a governmental university during the COVID-19 pandemic. To determine the sample size, consider the margin of error of 5%, power of 90%, medium effect size, and eleven predictors; the recommended sample size is 152. However, the final recruited sample was 143 students with a 62% survey response rate. The sole inclusion requirements were that students could access online surveys and enroll in a bachelor’s degree program at a university. It is important to mention that there were no prior diagnoses of Internet addiction or cyberchondria among the students.
Data were collected using an English online Google Form survey. Participants were provided a consent form statement of “answering the survey constitutes your consent” to ensure their voluntary participation. The Ethics Committee at the university where the researchers are currently working approved the study. Utilizing Facebook and WhatsApp, students were invited to answer the survey and invite their contacts as well. Anonymity and confidentiality were also assured by formatting the survey, enabling the students can leave the study before submitting the survey. Collected data were coded and then sorted in a password-protected Google Drive and computer, respectively. Also, Cookie-Based duplicate protection was selected to prevent duplicate responses to the survey.
The sociodemographic details were obtained through the electronic survey instrument.
2.3.1 Cyberchondria: Short Cyberchondria Severity Scale (SCS)
The original 33 CSS items measure health anxiety resulting from the excessive online search for health-related information.14 The short eight items of CSS (SCS) were used in the current study.37 It is a five-point Likert scale ranging from 1 to 5 (1 = Never, 2 = Seldom, 3 = Sometimes, 4 = Frequently, and 5 = Always). A mean of the total score above 24 indicates a high level of cyberchondria. Cronbach αs of the SCS ranged from .73 to .77,37 and its convergent validity was supported. In the current sample, the internal consistency of the SCS is 0.68; the low reliability could be related to the short scale or homogeneity of subjects’ responses.
2.3.2 Internet addiction test (IAT)
The 20 items of IAT assess the pathological Internet use symptoms in terms of presence and severity.38 It is a six-point Likert scale ranging from 0 to 5 (0 = not applicable, 1 = rarely, 2 = occasionally, 3 = frequently, 4 = often, 5 = always). Total scores of 0–30 points indicate a normal level of Internet usage, 31–49 indicates a mild level of Internet addiction, 50–79 means a moderate level, and 80–100 signifies a severe level.38 The scale has excellent internal reliability with a Cronbach α of .93 (Turkish version)39, 40 and .85 (Indonesian version),10 and 0.85 in the current sample.
2.3.3 Data analyses
At p < .05, the descriptive statistics of means and standard deviations or frequencies and percentages were used to describe the variables using the Statistical Package for the Social Sciences (SPSS) version 25.41 Cyberchondria and Internet addiction levels were assessed among students’ means and standard deviations based on total scores. The relationships among the concepts and sample characteristics were evaluated using the Pearson correlation coefficients. Standard multiple linear regressions were conducted to examine whether cyberchondria and sample characteristics (independent variables) predict Internet addiction (the dependent variable) while controlling for variations in the sample’s characteristics. These characteristics were age, gender, years of using the Internet (years online), income, availability of Internet at school, level of education, academic level (senior vs. junior) and stream, GPA, and whether they had infected with COVID-19 or not. All variables were treated as categorical variables and were dummy coded for the predictive regression model.
3.1 Sample’s characteristics and concepts’ scores
The majority of the sample were young females; senior nursing students studying for a bachelor’s degree from the scientific stream had Internet at school and did not get infected with COVID-19. Participants were familiar with Internet content for less than 4 years, had a GPA of 2.5 or more (very good), and came from middle-income families (Table 1). The current sample had a total mean score of CSS of 20.46 ± 4.80, indicating a moderate level of cyberchondria, while the total score of the IAT scale was 43.14 ± 13.00, indicating a mild level of Internet addiction. The majority of subjects fell in the low level of cyberchondria (≤24) and the mild Internet addiction category (Table 1). None of the subjects in our sample was classified in the severe Internet addiction category.
|<25 years||124 (86.7%)|
|25–34 years||9 (6.3%)|
|35 years and above||10 (7.0%)|
|≤4 years||95 (66.4%)|
|>4 years||48 (33.6%)|
|GPA ≤ 2.5||41 (28.7%)|
|GPA > 2.5||102 (71.3%)|
|≤600 JD||91 (63.7%)|
|>600 JD||52 (36.4%)|
|Type of students|
|Nursing students||112 (78.30%)|
|Other students||31 (21.70%)|
|Other students’ colleges|
|Science and engineering||11 (36.70%)|
|Art and educational sciences||6 (20.00%)|
|Sport and tourism||3 (10.00%)|
|Level of students|
|Level of education|
|Bachelor’s degree||121 (84.60%)|
|Master and doctorate||5 (3.50%)|
|Had Internet at school|
|Got infected with COVID-19|
|The total score of cyberchondria scale||20.46 ± 4.80|
|Low level of cyberchondria (≤24)||115 (80.4)|
|High level of cyberchondria (>24)||28 (19.6)|
|The total score on the Internet addiction scale||43.41 ± 13.00|
|The normal level of Internet usage (0–30)||19 (13.3%)|
|Mild Internet addiction (31–49)||75 (52.4%)|
|Moderate Internet addiction (50–79)||49 (34.3%)|
|Severe Internet addiction (80–100)||0 (0%)|
- Abbreviations: x̄, mean; SD, standard deviation.
- *Some totals do not equals to 134 because of missing data.
3.2 Correlations among the studied concepts
At a significance level of 0.01 using the Pearson correlation coefficient, significant positive moderate correlations were found between cyberchondria and Internet addiction (r = .587, results not shown) and whether the students got infected with COVID-19 or not (r = .356). Significant positive weak correlations were detected between Internet addiction and whether the students got infected with COVID-19 or not (r = .258). Those who get infected with COVID-19 are no longer obsessive with searching the Internet for COVID-19-related information; they got the experience itself; they will not keep experiencing cyberchondria and Internet addiction.
3.3 Predictors of Internet addiction
The standard multiple linear regression analysis results indicated that cyberchondria (β = .568, p < .001) and not having Internet at school (β = −0.212, p < .029) predicted Internet addiction. The model was significant (F (df = 14, 128) = 4.578 p < .001, Table 2), and it explained 35.8% of the variance in the score of Internet addiction. Cyberchondria is a risk factor for Internet addiction; searching repetitively for online medical information tends to result in Internet addiction. Not having Internet at school tends to decrease Internet addiction, which means that as the students do not have Internet access at the school or are not aware of its’ presence there, they will not be obsessive with the Internet; in turn, they will have low cyberchondria and mild Internet addiction.
|Predictors||Ba||βa||t test||p||R2||Adjusted R2||F testb (p value)|
|Years online: >4 years||0.450||.016||0.173||.863|
|Income: >600 JD||−1.674||−.061||−0.633||.529|
|Internet at school: no||−9.561||−.212||−2.230||.029|
|Education: master and doctorate||−3.343||−.038||−0.375||.709|
|Age: 25–34 years||0.841||.015||0.158||.875|
|Age: 35 years and above||−1.913||−.031||−0.299||.766|
|Academic stream: humanitarian||5.358||.096||1.022||.310|
|Academic level: junior||3.648||.109||1.142||.257|
|Type of students: others||−0.880||−.030||−0.315||.753|
|Got infected with COVID-19: no||0.293||.010||0.101||.920|
This study investigated students’ cyberchondria and Internet addiction levels and determined whether cyberchondria predicted Internet addiction. Cyberchondria in the current study was moderate, reconcilable with McElroy and Shevlin.14 The subjects had a mild level of Internet addiction, similar to other studies,38 and is expected to increase in light of the spurt of technology and information management systems.31, 32, 39 The moderate level of cyberchondria and mild Internet addiction level in the current sample could be related to the fact that most of the sample was nursing students; they have a solid background in health-related information.
Correlations between cyberchondria and Internet addiction were consistent with previous studies.1-3, 10, 12, 16, 17, 42 A prolonged Internet search for health-related information disrupts other Internet searches and other daily activities;39 thus, cyberchondria may lead to Internet addiction.22 Originating from fear and anxiety related to COVID-19, current students may keep surfing the Internet for COVID-19 health-related information,39 resulting in Internet addiction. A repetitive Internet search for health-related information such that related to COVID-19 is a feature of cyberchondria.31, 32, 39 Internet addiction correlated with whether the students got infected with COVID-19 or not. The students who have already experienced the disease will not extensively search it as much as those who did not yet infect.
4.1 Predictors of Internet addiction
Cyberchondria predicted Internet addiction; cyberchondria is a risk factor for Internet addiction, contrary to previous research.8, 9, 19-21 Internet addiction was reported to have a mediated influence on cyberchondria through using the Internet for seeking health information.43 However, the researchers assumed the opposite in the current study: the excessive search for online health information, the definition of cyberchondria, results in spending more time online and exhibiting more compulsive behaviors, leading to Internet addiction.25-28 The current reported results are not frequently reported in the literature. Notably, cyberchondria is a form of Internet addiction that is becoming more prevalent in academia because more students are obsessively searching the Internet for health-related information and course-related issues and assignments.27, 44 Cyberchondria is a vulnerability factor for anxiety during the COVID-19 pandemic because the Internet is rife with misinformation.4, 16 Consequently, cyberchondria will result in Internet addiction because people will continue surfing the Internet to satisfy their health-related curiosity.
Students who reported not having Internet at school tend to have a mild Internet addiction which low cyberchondria precedes; this is a self-explanatory result. However, it is worth mentioning that the Internet is available for all university students in Jordan; such reporting of not having Internet at school could be related to how the students are involved in their campuses. This result was a surprise also taking into consideration that the majority of the students were senior ones.
4.2 Limitations and implications
Although it is apparent in the current study that cyberchondria on Internet addiction, the study has many limitations. As most of the sample was university students, the effect of education on cyberchondria and Internet addiction was not assessed. The used scales were self-assessment tools; thus, they have to be used with other forms such as observations and interviews. The use of a cross-sectional design is the main limitation of the current project, which may impact the generalizability of its results. Also, the convenience snowball and the small sample, even with frequent reminders, used in the current study restrict the generalization of the results.
The model of Internet addiction was significant and explained only 35.8% of the variance in the Internet addiction score. Thus, longer scales and other variables should be studied in further studies.
More research should explore other personal and contextual variables influencing Internet addiction using a more extensive randomized sample from other universities and schools. Nurse educators should constantly integrate the Internet into education; e-learning at the time of data collection responded to the COVID-19 pandemic. In partnership with the Ministry of Health, the Ministry of Higher Education should give information to students to assist them in maintaining their mental health during stressful periods like COVID-19. Recommendations should accompany this information on how to manage stress and anxiety during challenging situations.
5 SUMMARY AND CONCLUSION
The current study explored the predictive role of cyberchondria on Internet addiction. Students experienced a moderate level of cyberchondria and a mild level of Internet addiction. Significant moderate correlations were reported among the studied concepts with each other and with the sample’s characteristics. Cyberchondria predicted Internet addiction, contrary to previous studies. Cyberchondria and Internet addiction studies are steadily increasing.
The researchers would like to acknowledge the input of all subjects who participated in the study.
CONFLICT OF INTERESTS
The authors declare that there are no conflict of interests.
The study was approved by the Institutional Review Board of the Hashemite University- Jordan.