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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 10  |  Issue : 2  |  Page : 200-206

Prevalence and predictors of cyberchondria and depression amid COVID-19 pandemic in adult population of Uttar Pradesh, India


1 Vivekananda College of Nursing, Lucknow, Uttar Pradesh, India
2 Yatharth Nursing College and Paramedical Institute, Chandauli, Uttar Pradesh, India
3 All India Institute of Medical Sciences, New Delhi, India

Date of Submission04-May-2022
Date of Acceptance26-Jun-2022
Date of Web Publication23-Dec-2022

Correspondence Address:
Mr. Surya Kant Tiwari
Yatharth Nursing College and Paramedical Institute, Chandauli - 232 104, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/amhs.amhs_95_22

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  Abstract 


Background and Aim: COVID-19 pandemic and related measures of containment such as lockdown led to heavy reliance on Internet for education and leisure activities. This study aimed to assess the prevalence and predictors of cyberchondria and depression amid COVID-19 pandemic among adult population of Uttar Pradesh, India. Materials and Methods: A web-based study was conducted among 236 adult participants of Uttar Pradesh during August–November 2021. Standardized tools including Cyberchondria Severity Scale-short version (CSS-SV) and Patient Health Questionnaire-9 were used for eliciting details about cyberchondria and depression. Results: The study highlighted that the cyberchondria cluster comprised 45.3% of participants. Subscale score distribution of CSS-SV was found to be the highest (7.53 ± 2.98) for excessiveness and the lowest (6.40 ± 2.93) for compulsion subscale. Furthermore, majority (56.4%) of the participants had depression in various severities. Multivariate logistic regression analysis predicted factors such as female gender, health-care worker, and duration of watching TV and smartphone usage to be influencing cyberchondria. Strong influence of duration spent (>6 h/day) in watching TV, etc., was found on depression. Conclusion: Cyberchondria and its association with depression are indeed growing health concerns; efforts should be directed toward controlled Internet usage, which involves adhering to credible sources for authentic health-related information.

Keywords: Adult population, COVID-19, cyberchondria, depression, predictors, prevalence


How to cite this article:
Srivastava SP, Tiwari SK, Negi M. Prevalence and predictors of cyberchondria and depression amid COVID-19 pandemic in adult population of Uttar Pradesh, India. Arch Med Health Sci 2022;10:200-6

How to cite this URL:
Srivastava SP, Tiwari SK, Negi M. Prevalence and predictors of cyberchondria and depression amid COVID-19 pandemic in adult population of Uttar Pradesh, India. Arch Med Health Sci [serial online] 2022 [cited 2023 Jan 31];10:200-6. Available from: https://www.amhsjournal.org/text.asp?2022/10/2/200/364978




  Introduction Top


The coronavirus illness 2019 (COVID-19), which was originally discovered in Wuhan, China in December 2019, has posed an incomprehensible threat to the international world. The virus spread quickly after its inception, resulting in millions of deaths and a negative impact on people's physical health, social lives, economic uncertainties, and mental health. To prevent the spread of COVID-19, governments around the world have limited people's freedoms by mandating them to stay at home, only permitting office work under particular conditions, and implementing tight “physical distance” laws.[1]

Under the strict lockdown conditions, increased uncontrolled usage of the Internet emerged as an even more problematic issue to tackle.[2] It was seen that online gaming, gambling activities, social media, and online pornography were especially on the rise during the COVID-19 pandemic.[3],[4],[5],[6] Furthermore, excessive online health searches have emerged as one of the dangerous online practices that are especially relevant during public health emergencies. These activities are mostly related to gathering information regarding COVID-19 infection.[7] Excessive online searches result in unrestrained anxiety and fear for the disease and push people into compulsive Internet searching for gathering information, which can lead to never-ending anxiety, creating a vicious cycle of cyberchondria.[8],[9] Furthermore, most of the information through online searches are conveyed rapidly causing ambiguity among people and adding to cognitive overload, as evidenced by prior research linking cyberchondria to cognitive overload and uncertainty.[10]

Cyberchondria can be conceptualized as anxiety-inducing consequences of health-related Internet searches. Regarding the term's origin (as the digital age's complement to hypochondria), cyberchondria refers to an unusual pattern of behavior and emotional state.[11] It is marked by anxiety as well as a compulsive component, reflecting that it is a multifaceted disorder.

Because most people have easy access to the Internet, whether, through a computer or a smartphone, cyberchondria became a major concern. A large amount of medical information available on the Internet can help people who are not health-care professionals to gain a better understanding of their health and disease states, as well as provide them with plausible explanations for their symptoms. If used for a diagnostic purpose, however, the web search has the potential to raise psychological health issues in individuals with limited medical awareness.[12]

The global average Internet penetration rate is 63.2%, suggesting that the Internet has established itself as one of the primary means for delivering targeted messages to audiences.[13] According to a survey of more than 12,000 people performed across 12 nations, the Internet has become an alternative for a health practitioner. Nearly half of those queried used “Google” as a search engine for self-diagnosis.[14] In India, cyberchondria is an emerging public mental health problem. A recent study highlighted the prevalence of cyberchondria to be present in 55.6% of employees in the information technology sector.[15]

Impact of COVID-19 pandemic is not only limited to physical symptoms but also includes psychological symptoms such as depressive disorders and anxiety. Approximately 4 in 10 adults in the United States have reported anxiety or depressive symptoms during the pandemic.[16] In India, the condition was even worse with 41% of adults suffering from anxiety or depression.[17] Mental illness during COVID-19 proved to be multifactorial; many underlying conditions such as loss of job, outdoor restrictions, fear of infection, and history of previous mental illnesses were found to be the major culprits.[17] Many other factors are still to be investigated.

Furthermore, literature focusing on the relationship between cyberchondria and depression is still scarce in India.[18] This survey aims to unveil the association between cyberchondria, depression, and various other variables, which would be helpful in devising preventive measures for the adult population, especially during the phase of crisis such as pandemic situation or any health-care emergency.


  Materials and Methods Top


Study design

A web-based survey was conducted between August and November 2021 among selected districts of Uttar Pradesh in India, to assess the prevalence and predictors of cyberchondria and depression amid COVID-19 pandemic. The questionnaire was created through Google Forms and was distributed through WhatsApp and e-mail. Following the principle of snowball sampling, respondents were requested to forward the link to their relatives/friends and colleagues. Participants were included in the study if they were of age 17 years or older and understood English language. Those currently suffering from any severe mental illnesses were excluded from the study. Participants were informed about the purpose of the study; informed consent and assent were taken from the participants. Parents consented when assent was taken. They were given freedom to withdraw at any point of time during the study. It took approximately 15 min to complete the questionnaires. Confidentiality and anonymity of the participants were maintained.

Sample size

Sample size of the study was calculated by taking the prevalence of cyberchondria equal to 55%[15] and precision of 7% at 95% confidence interval. Calculated sample size came out to be 198. However, taking 15% nonresponse rate, the final sample size was rounded-off to 230.

Tools for data collection

The data were collected using self-structured and standardized questionnaires. Datasheet was created to elicit information related to sociodemographic variables such as age, gender, area of residence, marital status, current educational level, and occupation and details related to COVID-19 infection.

Cyberchondria was assessed using standardized Cyberchondria Severity Scale-short version (CSS-SV), which is a shorter version of CSS having originally 33 items. The tool is divided into four subscales: (1) excessiveness (questions 1, 3, and 6) – measures escalating/repeated nature of search; (2) compulsion (questions 2, 7, and 10) – assesses web searches interfering with other aspects of online and offline life; (3) distress (questions 4, 8, and 9) – assesses anxiety/distress as a result of searches; and (4) reassurance (questions 5, 11, and 12) – assesses searches driving individual to seek out professional medical advices. The tool has good psychometric properties, and internal consistency values for both the total scale and subscales were acceptable to excellent.[19]

Depression associated with cyberchondria was assessed using Patient Health Questionnaire-9 (PHQ-9). The PHQ-9 was used to determine the severity of depressive symptoms using a self-administered screening questionnaire. The questionnaire examines how often the subjects were bothered by any of the nine topics in the previous 2 weeks.[20]

Permission was taken for using the standardized tools in the study.

Scoring

For CSS-SV, participants were instructed to opt the scores which typically correspond to their perceived medical illness along a Likert scale ranging from 1 (never) to 5 (always). Total score ranges from 12 to 60 with a higher score corresponding to higher level of cyberchondria.[19]

Each item of PHQ-9 was scored on a scale of 0–3 (0 = not at all; 1 = several days; 2 = more than a week; and 3 = nearly every day). The PHQ-9 total score ranges from 0 to 27 (scores of 5–9 are classified as mild depression; 10–14 as moderate depression; 15–19 as moderately severe depression; and ≥20 as severe depression). PHQ-9 demonstrates good reliability and is available in the open domain.[20]

Statistical analysis

The data were collected and coded in master datasheets. Both descriptive and inferential statistics were used for the analysis. Each subscale of CSS-SV was treated as a distinct scale. Because CSS-SV lacks cutoff scores for categorization and analysis, k-mean cluster analysis was used to divide the participants' mean scores into two groups: cyberchondria cluster and normal cluster. The correlation of scores between the two clusters was explained using a scatter graph. The mean PHQ-9 scores in two clusters were compared using an independent samples t-test. Multivariate logistic regression analysis was used to predict the factors that influence cyberchondria and depression.


  Results Top


Study population

Out of 255 responses received, 19 responses were incomplete and screened out. Therefore, 236 participants were eligible for analysis. The mean age of the participants was 26.64 ± 8.38 years with majority (65.3%) being female. The majority of the participants lived in a city and belonged to a nuclear family. It was found that three-quarters (77.5%) of the participants had not tested positive for COVID-19 in the previous year. Over two-thirds (69.5%) of the participants reported that their friends or relatives had tested positive for COVID-19, and nearly a third (32.2%) said that they had lost a family member, relative, or friend to COVID-19 in the previous year. The majority of the participants in our study did not have any chronic conditions such as hypertension and diabetes [Table 1].
Table 1: Sociodemographic characteristics and COVID-19 related information of the participants (n=236)

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Cyberchondria severity

The cyberchondria severity of participants as measured by CSS-SV is explained in [Table 2]. [Table 3] explains subscale score distribution of CSS-SV, namely, excessiveness, compulsion, distress, and reassurance with mean ± standard deviation scores of 7.53 ± 2.98, 6.40 ± 2.93, 6.93 ± 3.09, and 6.70 ± 3.25, respectively. It was found that the mean score was the highest for excessiveness and the lowest for compulsion subscale.
Table 2: Cyberchondria Severity Scale responses of participants (n=236)

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Table 3: Score distribution of participants in four sub-scales of Cyberchondria Severity Scale-short version (n=236)

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The k-mean cluster analysis of the scores in the four domains of CSS-SV is explained in [Table 4]. The cluster with the highest cluster center on all the four dimensions was classified as cyberchondria cluster and the cluster with lower scores as the normal cluster. It reveals that 46.1% of participants were in cyberchondria cluster and 53.9% of participants were in normal cluster.
Table 4: Classification of study participants into cyberchondria and normal cluster

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Prevalence of depression

[Table 5] shows the prevalence of depression among study participants. It can be appreciated that majority (43.6%) of the participants had no depression, followed by mild (38.6%), moderate (11.0%), and only 3.8% of the participants were found to have moderately severe depression. Surprisingly, 7 (3%) of participants were suffering from severe depression.
Table 5: Level of depression among participants (n=236)

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Predictors of cyberchondria and depression

Scatter plot diagram found that participants having cyberchondria had greater cyberchondria severity scores as well as PHQ-9 scores. A significant weak positive correlation of 0.287 was found between cyberchondria severity score and PHQ-9 score for both the clusters.

[Figure 1] depicts that the median PHQ-9 score is slightly higher in the cyberchondria cluster in comparison to the normal cluster and the scores are more broadly dispersed in the cyberchondria group.
Figure 1: Box plot of the PHQ-9 scores for the cyberchondria and normal cluster, PHQ-9: Patient Health Questionnaire-9

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An independent samples t-test was used to compare both the clusters based on the mean scores of PHQ-9 and CSS, which yielded a significant difference between the mean PHQ-9 scores for each group, t (234) =4.727, P ≤ 0.001.

[Table 6] explains multivariate logistic analysis of factors influencing cyberchondria and depression. It was found that female gender, health-care worker as occupation, and time spent watching TV and social media of 1–3 h/day had significantly influenced cyberchondria severity. Depression was found to be influenced by duration (more than 6 h/day) of watching TV and using social media.
Table 6: Multivariate logistic analysis of factors influencing cyberchondria and depression

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  Discussion Top


This web-based cross-sectional study examined the prevalence and predictors of cyberchondria and depression amid COVID-19 pandemic. The study suggested that cyberchondria is a common problem in more than two-fifth of the individuals with major themes being excessive Internet searches and resulting distress due to excessiveness. Furthermore, more than half of the participants had some form of depression ranging from mild to severe. The study also highlighted a significant but weak positive correlation between cyberchondria severity scores and depression.

In this study, k-mean cluster analysis was done to classify the domain-wise cyberchondria severity scores into cyberchondria cluster and normal cluster. We have found that 46.1% of the participants belonged to cyberchondria cluster. A similar analytical approach was utilized by Makarla et al.[15] in their study to assess the predictors and correlates of cyberchondria among IT sector employees. Shailaja et al.[21] in their study among dental students reported 98.7% participants as having cyberchondria. They further emphasized that excessiveness and reassurance were the most common themes discovered among those found to be suffering from cyberchondria. This was partially consistent with the findings discovered in our study, in which subscale distribution of CSS-SV scores was predominantly reporting the phenomenon of excessiveness, followed by distress.

Utilization of Internet for education, shopping, and online gaming proved to be beneficial during COVID-19 pandemic times. Despite many advantages, there are numerous limitations such as problematic Internet usage and lack of authentic sources for accessing health-related information.[22] Therefore, there is a greater need to promote accurate information to battle with “infodemic.” In this regard, Kothari and Moolani[23] demonstrated positive impact of Google search and Internet on the knowledge of the patients regarding the etiopathogenesis and the defects due to the disease.

We further examined a relationship between cyberchondria and depression, which demonstrated a weak positive correlation between cyberchondria and depression scores; this was similar to the study conducted by Shailaja et al.[21] and Makarla et al.,[15] who reported higher depression scores and its association with cyberchondria. In addition, they also suggested that predisposition of an individual to develop cyberchondria depends on poor psychological status. Arsenakis et al.[18] reported a contrasting finding of negative association between PHQ-9 and cyberchondria scores.

The association of age and gender with cyberchondria is still scarce and inconsistent.[24] We tried to predict various factors influencing cyberchondria using multivariate regression analysis, and it was found that the female gender, health-care worker as occupation, and duration of 1–3 h/day watching TV and social media influenced cyberchondria severity.

A study done by Zarcadoolas et al.[25] proposed that the main theme of online search by the less educated people was medical information. Another study by Atkinson et al.[26] described that higher educational status in women influences more health information search in comparison to men and less educated people.

The COVID-19 pandemic is an undreamed-of-life experience that has squeezed everyone in different ways. The impact of COVID-19 pandemic on psychological health is very well documented.[27] Major health hazards identified relates to increased incidences of psychiatric disorders.[28],[29] In the current study, the researchers used PHQ-9 as a screening tool for depression. Results revealed that the majority of the participants were suffering from mild-to-severe depression, similar to a study done by Jalloh et al.[30] on Ebola patients, which showed that nearly half of the participants reported at least one symptom of anxiety or depression. Astonishingly, 6.8% of participants were having moderately severe to severe depression. In the present study, duration (more than 6 h/day) spent in watching TV, using social media, and Facebook had significantly influenced depression.

The results of this descriptive cross-sectional survey provide pertinent information about the emerging illness, i.e., cyberchondria and depression amid COVID-19 pandemic in Indian population. Due to the paucity of literature on cyberchondria and depression amid COVID-19 pandemic, we could not get sufficient studies to compare our research findings. Although, presently, there are no definite therapies tailormade for cyberchondria.[31] Still, Internet-based cognitive–behavioral therapies could be utilized to alleviate anxiety among individuals suffering from cyberchondria. A study reported optimism as a psychologically protective factor against cyberchondria and suggests enhancing and strengthening optimism during crisis situations such as during pre- and post-COVID-19 pandemic scenarios.[9]

This study highlighted a need for controlled and disciplined approach on the part of individuals while using the Internet. Furthermore, psychoeducation should be incorporated to promote online health-related information literacy.[1] Health-care professionals should utilize mental health screening tools during COVID-19 pandemic for identifying high-risk groups and systematically addressing their needs.[30]

The practical suggestions of the study are important for clinicians, mental health professionals, health-care systems, and policymakers. Various ways to tackle cyberchondria include enhancing e-literacy, advices to obtain health-related information from credible sources such as WHO would serve as the primary means to check hazardous effects of cyberchondria. There is a need to improve mental health of the adult population in the immediate future. The long-term psychological impact of the current pandemic is yet to be evaluated. There is an urgent need to plan studies on various factors contributing to cyberchondria-related illnesses, also link between various psychiatric conditions such as psychosomatic illnesses, obsessive–compulsive disorder should be discovered with the help of research.

To the best of the investigator's knowledge, this is the first study to examine the prevalence and predictors of cyberchondria and depression in relation to COVID-19 pandemic in the Indian adult population. In addition, we have managed to acquire an adequate sample size. Despite these strengths, there are some limitations also. First, being an online survey, systematic sampling bias and participant self-selection cannot be ignored. Second, a self-reported questionnaire was used to investigate cyberchondria severity and depression, and this may lead to biased responses. Furthermore, in terms of sociodemographic factors, the study comprises substantial samples; however, they are not representative of the overall population. In particular, the sample contained a higher number of women and people with higher levels of education than the total population. Furthermore, the study's online structure and form of participant recruiting (including via social media) were more likely to attract persons who had a stronger propensity for using the Internet. It is also likely that those who enjoy using the Internet and participating in online activities have greater overall levels of cyberchondria. Finally, temporal and causal relationships cannot be examined due to the cross-sectional nature of the study.


  Conclusion Top


Cyberchondria is a common problem that emerged during COVID-19 pandemic mainly due to uncontrolled Internet usage and various other factors. It has the potential to cause depression, which necessitates early detection and timely management. Controlled Internet usage, limited access to authentic content, yoga, meditation, and self-care activities on day-to-day basis can be used for the prevention of adverse health consequences. Furthermore, efforts should be made for developing consensus guidelines related to the assessment and prevention of cyberchondria and depression under crisis situations in India.

Acknowledgment

The researchers would like to acknowledge all the study participants without whom the study would never complete.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Varma R, Das S, Singh T. Cyberchondria amidst COVID-19 pandemic: Challenges and management strategies. Front Psychiatry 2021;12:618508.  Back to cited text no. 1
    
2.
Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, et al. Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Compr Psychiatry 2020;100:152180.  Back to cited text no. 2
    
3.
Håkansson A, Fernández-Aranda F, Menchón JM, Potenza MN, Jiménez-Murcia S. Gambling during the COVID-19 crisis – A cause for concern. J Addict Med 2020;14:e10-2.  Back to cited text no. 3
    
4.
Mestre-Bach G, Blycker GR, Potenza MN. Pornography use in the setting of the COVID-19 pandemic. J Behav Addict 2020;9:181-3.  Back to cited text no. 4
    
5.
King DL, Delfabbro PH, Billieux J, Potenza MN. Problematic online gaming and the COVID-19 pandemic. J Behav Addict 2020;9:184-6.  Back to cited text no. 5
    
6.
Wiederhold BK. Social media use during social distancing. Cyberpsychol Behav Soc Netw 2020;23:275-6.  Back to cited text no. 6
    
7.
Starcevic V, Schimmenti A, Billieux J, Berle D. Cyberchondria in the time of the COVID-19 pandemic. Hum Behav Emerg Technol 2021;3:53-62.  Back to cited text no. 7
    
8.
Jokic-Begic N, Lauri Korajlija A, Mikac U. Cyberchondria in the age of COVID-19. PLoS One 2020;15:e0243704.  Back to cited text no. 8
    
9.
Maftei A, Holman AC. Cyberchondria during the coronavirus pandemic: The effects of neuroticism and optimism. Front Psychol 2020;11:567345.  Back to cited text no. 9
    
10.
Fergus TA. Anxiety sensitivity and intolerance of uncertainty as potential risk factors for cyberchondria: A replication and extension examining dimensions of each construct. J Affect Disord 2015;184:305-9.  Back to cited text no. 10
    
11.
Starcevic V. Cyberchondria: Challenges of problematic online searches for health-related information. Psychother Psychosom 2017;86:129-33.  Back to cited text no. 11
    
12.
Starcevic V, Berle D, Arnáez S. Recent insights into cyberchondria. Curr Psychiatry Rep 2020;22:56.  Back to cited text no. 12
    
13.
World Internet Users Statistics and World Population Stats 2021. Available from: https://internetworldstats.com/stats.htm. [Last accessed on 2021 Sep 8].  Back to cited text no. 13
    
14.
Public Sector HR. Available from: http://www.epolitix.com/fileadmin/epolitix/stakeholders/Online_Health.pdf. [Last accessed on 2021 Sep 8].  Back to cited text no. 14
    
15.
Makarla S, Gopichandran V, Tondare D. Prevalence and correlates of cyberchondria among professionals working in the information technology sector in Chennai, India: A cross-sectional study. J Postgrad Med 2019;65:87-92.  Back to cited text no. 15
[PUBMED]  [Full text]  
16.
Panchal N, Kamal R. The Implications of COVID-19 for Mental Health and Substance Use; KFF; 2021. Available from: https://www.kff.org/coronavirus-covid-19/issue-brief/the-implications-of-covid-19-for-mental-health-and-substance-use/. [Last accessed on 2021 Oct 20].  Back to cited text no. 16
    
17.
Prevalence of Stress, Anxiety, Depression among the General Population during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis | Globalization and Health | Full Text. Available from: https://globalizationandhealth.biomedcentral.com/articles/10.1186/s12992-020-00589-w. [Last accessed on 2021 Oct 19].  Back to cited text no. 17
    
18.
Arsenakis S, Chatton A, Penzenstadler L, Billieux J, Berle D, Starcevic V, et al. Unveiling the relationships between cyberchondria and psychopathological symptoms. J Psychiatr Res 2021;143:254-61.  Back to cited text no. 18
    
19.
McElroy E, Kearney M, Touhey J, Evans J, Cooke Y, Shevlin M. The CSS-12: Development and validation of a short-form version of the cyberchondria severity scale. Cyberpsychol Behav Soc Netw 2019;22:330-5.  Back to cited text no. 19
    
20.
Test Review: Patient Health Questionnaire–9 (PHQ-9). Rehabilitation Counseling Bulletin 2014;57:246-8.  Back to cited text no. 20
    
21.
Shailaja B, Shetty V, Chaudhury S, Thyloth M. Exploring cyberchondria and its associations in dental students amid COVID-19 infodemic. Ind Psychiatry J 2020;29:257-67.  Back to cited text no. 21
  [Full text]  
22.
Benigeri M, Pluye P. Shortcomings of health information on the Internet. Health Promot Int 2003;18:381-6.  Back to cited text no. 22
    
23.
Kothari M, Moolani S. Reliability of “Google” for obtaining medical information. Indian J Ophthalmol 2015;63:267-9.  Back to cited text no. 23
[PUBMED]  [Full text]  
24.
Vismara M, Caricasole V, Starcevic V, Cinosi E, Dell'Osso B, Martinotti G, et al. Is cyberchondria a new transdiagnostic digital compulsive syndrome? A systematic review of the evidence. Compr Psychiatry 2020;99:152167.  Back to cited text no. 24
    
25.
Zarcadoolas C, Blanco M, Boyer JF, Pleasant A. Unweaving the Web: An exploratory study of low-literate adults' navigation skills on the World Wide Web. J Health Commun 2002;7:309-24.  Back to cited text no. 25
    
26.
Atkinson NL, Saperstein SL, Pleis J. Using the internet for health-related activities: Findings from a national probability sample. J Med Internet Res 2009;11:e4.  Back to cited text no. 26
    
27.
Torales J, O'Higgins M, Castaldelli-Maia JM, Ventriglio A. The outbreak of COVID-19 coronavirus and its impact on global mental health. Int J Soc Psychiatry 2020;66:317-20.  Back to cited text no. 27
    
28.
Di Crosta A, Palumbo R, Marchetti D, Ceccato I, La Malva P, Maiella R, et al. Individual differences, economic stability, and fear of contagion as risk factors for PTSD symptoms in the COVID-19 emergency. Front Psychol 2020;11:567367.  Back to cited text no. 28
    
29.
Raj S, Ghosh D, Singh T, Verma SK, Arya YK. Theoretical mapping of suicidal risk factors during the COVID-19 pandemic: A mini-review. Front Psychiatry 2020;11:589614.  Back to cited text no. 29
    
30.
Jalloh MF, Li W, Bunnell RE, Ethier KA, O'Leary A, Hageman KM, et al. Impact of Ebola experiences and risk perceptions on mental health in Sierra Leone, July 2015. BMJ Glob Health 2018;3:e000471.  Back to cited text no. 30
    
31.
Bajcar B, Babiak J. Self-esteem and cyberchondria: The mediation effects of health anxiety and obsessive–compulsive symptoms in a community sample. Curr Psychol 2021;40:2820-31.  Back to cited text no. 31
    


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