INTRODUCTION
Cyberchondria is a recent phenomenon that is an unhealthy behavioral pattern and an emotional condition. It is more than just a behavior of searching health-related information (HRI) on the internet; rather, it is the excessive/frequent searching of the internet for HRI that results in concerns/anxiety over health and wellness.[ 1 ] Studies have shown that in the recent past, there is an increase in the prevalence of cyberchondria.[ 2–5 ]
Compulsive use of the internet, which interferes with daily life activities, is the main feature of this disorder.[ 2 ] People with cyberchondria usually have poor health awareness, driving them to seek information about their condition and/or confirm their illness.[ 6 ] The excessive browsing for HRI, and the resultant overwhelming information obtained adds to the psychological distress of a person with cyberchondria.[ 7 ] Alternatively, some people over-search online until they subconsciously become symptomatic and/or feel ill.[ 8 ]
Cyberchondria has been linked to many factors such as age and gender.[ 9 ] In addition, addiction to smartphones, which are now widely available and provide easy access to information, is increasing; smartphone addiction is also a key contributing factor of cyberchondria.[ 9–12 ] Although the use of information and communications technology to support health and health-related fields, defined as electronic health (eHealth), can be beneficial,[ 13 ] it is dependent on an individual’s eHealth literacy, which is “the ability to seek, find, understand, and appraise health information from electronic sources.”[ 14 ] In fact, having high eHealth literacy may reduce the unwanted effects of cyberchondria.[ 15 ]
Few studies from the Middle East, including Saudi Arabia, have determined the prevalence of cyberchondria. The current study was conducted to fill this gap and aimed to assess the prevalence of cyberchondria and its association with smartphone addiction and eHealth literacy in a population from Saudi Arabia.
METHODS
Study design, setting, and participants
This cross-sectional study included adult Saudis living in Jeddah, Saudi Arabia, and was conducted between May 1 and June 30, 2022. The study was approved by the Institutional Review Board of Fakeeh College of Medical Sciences, Jeddah.
Volunteer sampling was used for recruiting participants. A four-section questionnaire was distributed using Google Forms. The link to the questionnaire was shared to individual accounts and on groups of social interest in different social networks, including Facebook, Twitter, and WhatsApp.
Inclusion criteria were being aged ≥18 years, Saudi national, and literate (i.e. able to read and write in Arabic). Exclusion criteria were all healthcare providers, students of medical sciences, and those with applications for contacting healthcare providers to gain knowledge, as their situations allow them to handle the HRI on the internet in a different way. A brief section at the beginning of the questionnaire determined the inclusion/exclusion of the respondent.
All participants were required to provide a consent for inclusion. An introduction section in the questionnaire informed the participants about the aim of the study, that participation was voluntary, and that the obtained data would be confidential and only used for current research purposes. In addition, all responses were collected anonymously, and the data of the participants were coded and managed using these codes. No incentives were offered for participation.
Only responses without any missing data were included in the final analysis. The usability and technical functionality of the electronic questionnaire was tested by the authors and their colleagues before it was distributed. To avoid duplicate submissions, by selecting an option in Google Forms, participants were informed that their response had already been submitted in case they attempted resubmission.
Sample size calculation
The sample size was calculated by the Epi-info software (version 3.01; Centers for Disease Control and Prevention, USA) using the prevalence of cyberchondria among employees working in information technology in Chennai, India (55.6%), in which the population were of diverse education levels (diploma, bachelors, masters, and doctorate), similar to the population that was being targeted for the present study.[ 3 ] A recent systematic review and meta-analysis on response rates of online surveys found that the non-response rate is about 11%–12%.[ 16 ] Accordingly, we assumed a non-response rate of 10%, and the sample size was calculated to be 418 participants. However, the above-stated meta-analysis also revealed that the estimates of the data remained reliable with a sample size of at least 500, and thus the authors targeted a larger population to achieve the same.
Data collection tools
The questionnaire included the following four sections:
General characteristics of the studied population: age, gender, education, marital status, occupation, and any chronic diseases.
The 12-item Cyberchondria Severity Scale (CSS) was used to determine cyberchondria and its severity.[ 17 ] CSS includes four constructs, namely, compulsion, distress, excessiveness, and reassurance. Each construct is represented by three questions with a 5-point Likert response format. Scores on each item range from 1 (Never) to 5 (Always), with total scores ranging from 12 to 60, and higher scores representing higher severity. The scores were categorized as follows: scores ≤25th percentile were considered as low, between >25th and <75th percentile as moderate, and ≥75th percentile as high cyberchondria.[ 18 ] The CSS scale has been validated in a different population and found to have good reliability (Cronbach’s alpha = 0.9).[ 17 ]
The 10-item Smartphone Addiction Scale-Short Version (SAS) was used to determine smartphone addiction. Each question had a 6-point Likert response format, and scoring on each item ranged from 1 (Strongly Disagree) to 6 (Strongly Agree) (total score range: 10–60). The cut-off score for defining smartphone addiction differs according to gender: ≥31 for males and ≥33 for females. The SAS scale has been validated in a different population and found to have excellent reliability (Cronbach’s alpha = 0.967).[ 19 ]
The 8-item Electronic Health Literacy scale (eHEALS) was used to estimate individual’s perception of own eHealth-literacy. The scale comprises 8 questions, each question had five options in a 5-point Likert response format. Scores on an item range from 1 (Strongly Disagree) to 5 (Strongly Agree). The total scores of the eHEALS range from 8 to 40, with higher scores representing higher self-perceived eHealth literacy. The cutoff score for defining high eHealth literacy is ≥26.[ 20 ] The eHEALS scale has been validated in a different population and found to have excellent reliability (Cronbach’s alpha = 0.94).[ 21 ]
The forward–backward technique was used for translating CSS, SAS, and eHEALS to Arabic. First, the authors did a forward translation (i.e. from English to Arabic). An independent, proficient English and Arabic speaker (who conducts lectures in Arabic and English languages in Fakeeh College for Medical Sciences) first did a back translation (from Arabic to English), and then did a second forward translation (from English to Arabic). A certified Arabic–English translator compared the original and back translated versions of the English questionnaire, while a professor of medicine compared the two translated Arabic versions. All discrepancies were resolved, and both the final Arabic translated versions were matched for cohesiveness.
The final Arabic version was evaluated for content validity by panel comprising experts in the field of psychiatry, pharmacology, and public health, and their opinion was that the tool accurately covered the concept it purported to measure. To ensure the clarity and simplicity of the questions and answer choices, the questionnaire was then evaluated for face validity through a pilot study. In total, 30 participants were interviewed, and based on their feedback, it was concluded that the translated questionnaire was clear, simple, and well understandable. The reliability of the Arabic version among the Saudi population was measured using Cronbach’s alpha.
Data management and analysis
Data were coded, entered, and managed using Microsoft Excel (2019 version). Data analyses were performed using IBM SPSS software (version 25). Cronbach’s alpha was assessed for each translated scale and a value of >0.7 was considered acceptable. Descriptive statistics were presented by frequencies and percentages and mean ± SD (range). Pearson correlation was used to assess the association between quantitative variables. Student’s t test was used to compare means between two groups, and one-way ANOVA test for comparing means between more than two groups. Multivariable linear regression analysis was used to assess predictors of cyberchondria. A sensitivity analysis was separately conducted for male and female participants to compare any variation in results using the corresponding statistical tests and P value. P value <0.05 was considered statistically significant based on the level of confidence of 95%.
RESULTS
The reliability of the Arabic versions of the questionnaire was considered satisfactory, as the Cronbach’s alpha was >0.7 (CSS = 0.882; SAS = 0.887; eHEALS = 0.903).
A total of 518 participants completed the questionnaire, with a mean (±SD) age of 33 (±14) years. Most of the participants were females (64.1%), had completed or were pursuing university-level education (77.4%), and single (54.6%). About one-third of the participants were students (34.7) and had at least one chronic illness (36%); 7.7% had multiple chronic diseases. Diabetes mellitus was the most commonly reported chronic illness (5.8%) [Table 1 ].
Table 1: General characteristics of the study participants (N =518)
The mean of the total CSS score was 34.6 ± 10, while its domain-wise scores were as follows: excessiveness, 11 ± 3; distress, 9.4 ± 3; reassurance, 8.2 ± 3; and compulsion 5.9 ± 3. The prevalence of cyberchondria was 2.1% (CI = 1.1–3.8) for low, 83.4% (CI = 79.9–86.5) for moderate, and 14.5% (CI = 11.6–17.8) for high grades. Two-thirds of the participants (66.6%) had smartphone addiction, while three-fourths (72.6%) had a high level of eHealth literacy [Table 2 ].
Table 2: Cyberchondria, smartphone addiction and electronic health literacy of the participants
Correlation and predictors of cyberchondria
The relation between CSS score and the general characteristics showed that females, widows and divorcees, and students had higher scores of CSS (P < 0.05) [Table 3 ]. There were positive significant correlations between the CSS score and both SAS (r = 0.395, CI = 0.316/0.475, P = 0.0001) and eHEALS (r = 0.265, CI = 0.182/0.349, P = 0.0001) scores.
Table 3: Relation between cyberchondria and general characteristics of the studied participants
In the multivariable linear regression model, older age was a significant predictor of lower cyberchondria scores (β = −0.279, CI = −0.279–−0.103), while being divorced and widow (β =0.14, CI = 0.612 − 4.271), having a smartphone addiction (β =0.329, CI = 0.224–0.363), and eHealth literacy (β =0.163, CI = 0.133 − 0.381) were significant predictors of higher cyberchondria scores [Table 4 ].
Table 4: Multivariable linear regression analysis of predictors to cyberchondria among the studied participants
In the multivariate stepwise linear regression model, interaction between the significant predictors accounted for a small variance: between age and marital status (R2 = 8.9%); age, marital status, and smartphone addiction score (R2 = 21.5%); and age, marital status, smartphone addiction score, and eHealth literacy score (R2 = 24.1%) [Table 5 ]. A variance inflation factor (VIF) was conducted to assess the correlation among independent variables that were statistically affecting cyberchondria in the regression analysis. Both the VIF and tolerance statistics met the recommended cut-off points of <10 and >0.1, respectively.
Table 5: Stepwise multivariate linear regression model of predictors to cyberchondria among the studied participants
In a sensitivity analysis for male and female participants [Suppl. Table 1 ], females were found to have significantly higher eHealth literacy and a non-significantly higher smartphone addiction than males. No significant difference relation was noted between cyberchondria and education level for both genders [Suppl. Table 2 ]. Cyberchondria scores were higher among males who were divorced [Suppl. Table 3 ], among male students and retired females [Suppl. Table 4 ], and among females with chronic illness [Suppl. Table 5 ]. Similar to the total sample, in both genders, significant correlations were found between cyberchondria score and smartphone addiction and electronic health literacy [Suppl. Table 6 ]. In the regression model, only marital status for males, and age and marital status for females, were not significant predictors of cyberchondria score, which was in contrast to the findings in the total population. [Suppl. Table 7 ].
Table 1: Comparison of smartphone addiction and electronic health literacy based on gender
Table 2: Relation between cyberchondria and education level of males and females
Table 3: Relation between cyberchondria and marital status
Table 4: Relation between cyberchondria and occupation
Table 5: Relation between cyberchondria and presence of chronic illness
Table 6: Correlations between cyberchondria and smartphone addiction and electronic health literacy
Table 7: Multivariable linear logistic regression analysis of factors affecting cyberchondria
DISCUSSION
The current study revealed a high prevalence of cyberchondria and a significant association between smartphone addiction and cyberchondria severity. These findings were consistent with those of previous studies.[ 12 , 22–24 ] The interaction between these three domains could possibly result in significant mental burden: being addicted to smartphones can aid cyberchondria, while having a high eHealth literacy can result in believing the self-diagnosis.
Smartphones are, in many developing countries, affordable and widely used, including by children.[ 25 ] The relative affordability of getting HRI online causes the repeated behavior of searching the internet to feel comfortable, sometimes for the same health concerns. The recent and huge digital transformation in Saudi Arabia depends on having a communication device, usually a smartphone with an internet connection.[ 26 ] The prevalence of internet addiction was reported in previous research to be up to 41.1% among school/university students and adults in Saudi Arabia.[ 27 ]
Another possible explanation is “physician shopping,” which is the intense desire of visiting multiple doctors for the same concern. To avoid the additional costs associated with such behavior, searching the internet tends to be an alternative.[ 28 ] Moreover, the current COVID-19 pandemic increased the rate of searching the internet for health concerns.[ 23 ] People with high eHealth literacy can comprehend the HRI they find on the internet.[ 29 ] However, the current study reported that there is a statistically significant association between high eHealth literacy and cyberchondria, which is in agreement with the findings of previous studies.[ 15 , 30 ] This may be explained by the fact that eHealth literacy is directly proportional to the time spent on, and the frequency of searching, the internet.[ 31 ]
In terms of the domains of cyberchondria domains, compulsion had the lowest mean score, while excessiveness had the highest. The high level of eHealth literacy among the studied participants is a possible explanation, as it reduces compulsiveness. Excessiveness, the leading sign of cyberchondria, is the domain that drives the existence and magnitude of distress and anxiety that creates and interacts with the other domains to negatively affect mental health.[ 32 ] Its highest score could be also explained by the high prevalence of smartphone addiction among the studied participants.
The female and student participants in the current study had a higher severity of cyberchondria, which is consistent with the findings of previous studies.[ 33 , 34 ] Females search the internet for any unexplained bodily sensation significantly more commonly and frequently than males.[ 34 ] Again, those with a higher level of education, or being a student at the university, are likely to believe that the frequent searching of the internet for HRI may prepare them well for the clinic’s visit in case of illness, or in promoting health and leading a healthy lifestyle, and that they may be able to assist the clinician in diagnosis and management.[ 35 ]
The current study also revealed that the CSS scores were almost the same between non-working and currently working participants, which were higher than that of retired respondents. In contrast, Ciułkowicz et al .[ 36 ] reported that occupational inactivity results in limited access to healthcare, which in turn creates an economic burden on these individuals. A possible explanation for this difference is that in Saudi Arabia, citizens are provided healthcare for free, and thus it has a fair coverage for those non-working, while the pension for retirees adequately covers any additional healthcare needs.[ 37 ]
The sensitivity analysis for male and female participants to compare any variation in results showed that females had significantly higher eHealth literacy and a non-significantly higher smartphone addiction than males. Similarly, Perry and Lee[ 38 ] also did not find any significant gender-related differences in smartphone addiction, while Tennant et al .[ 39 ] found that females had a higher level of eHealth literacy than males. Kurcer et al .[ 40 ] found that health anxiety scores were significantly higher among those who lived alone and with a chronic disease. Health anxiety is also the main driver for cyberchondria. Similarly, the current study found that females with chronic illness and males who were divorced had higher scores of cyberchondria.
Limitations
The primary limitation of the current study is the cross-sectional design, which, despite its suitability for the objective of the study, limits elucidating the temporal relationship between influencing factors and cyberchondria. In addition, a single point data collection cannot assess changes in the population over a period of time or detect trends. The volunteering bias and the lack of random sampling may have resulted in the sample not being adequately representative of the population. In addition, the study did not determine if the respondents had any pre-existing psychiatric or psychosomatic conditions, which could result in differing patterns of cyberchondria.
CONCLUSION
The study revealed a high prevalence of cyberchondria in a Saudi population, and this was associated with smartphone addiction and high eHealth literacy. Cyberchondria severity was also influenced by age and marital status. Therefore, future studies on cyberchondria are required in Saudi Arabia to better characterize the condition and accordingly take preventive measures. These studies could also focus on potential influencing factors on cyberchondria such as years of work/employment, income, and other socioeconomic variables.
Ethical considerations
The study was approved by the Institutional Review Board of Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia (approval No.: 295/IRB/2022; date: February 3, 2022). All participants provided informed consent before participation. The study adhered to the principles of Declaration of Helsinki, 2013.
Data availability statement
The datasets generated and/or analyzed during the current study are not publicly available but can be obtained from the corresponding author on reasonable request.
Peer review
This article was peer-reviewed by two independent and anonymous reviewers.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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