Cyberchondria Severity Scale

1. Abstract

The Cyberchondria Severity Scale (CSS) is a psychometric instrument designed to assess the behavioral compulsions, emotional distress, and functional impairments associated with excessive, repetitive online health information seeking. Conceived and validated by psychometric researchers Eoin McElroy and Mark Shevlin at the University of Ulster, the CSS addresses the clinical and psychological sequelae of digital symptom searching, commonly referred to as “cyberchondria.” While access to digital medical resources holds the potential to empower patients, individuals vulnerable to health anxiety often experience an escalation of medical dread, cognitive intrusive thoughts, and safety-seeking spirals when interacting with ambiguous online diagnostic information. The CSS was engineered to capture the multidimensional nature of this phenomenon, moving beyond crude single-item indicators to establish a continuous, standardized measure of symptom severity.

Initially derived from an exploratory pool of 43 candidate items, rigorous psychometric refinement via parallel analysis and exploratory factor analysis (EFA) isolated a correlated five-factor structure comprising 33 items. These five primary dimensions include: (1) Compulsion, which evaluates the perceived loss of control and behavioral urges driving uninterrupted symptom searches; (2) Distress, capturing subjective experiences of panic, apprehension, and existential dread induced by digital medical content; (3) Excessiveness, measuring the disproportionate duration, frequency, and breadth of online searches; (4) Reassurance Seeking, indexing paradoxical attempts to alleviate physical fears through iterative searching or consultations; and (5) Mistrust of Medical Professional, which operationalizes skepticism and relational conflict occurring between patients and physicians due to self-directed web diagnostics. Respondents rate items using a 5-point Likert scale ranging from 1 (“Never”) to 5 (“Always”).

Psychometric evaluations demonstrate high internal consistency across subscales (Cronbach’s α typically ranging from .75 to .95) and strong convergent validity with established measures of negative affect and somatic preoccupation, including the Depression Anxiety Stress Scales (DASS-21) and the Short Health Anxiety Inventory (SHAI). The CSS has become an international standard in behavioral medicine, psychiatric epidemiology, and clinical psychology, offering researchers a validated metric to assess the etiology and economic burden of digital somatization, while equipping clinicians to diagnose and target maladaptive internet-driven safety behaviors through evidence-based cognitive-behavioral therapies.

2. Keywords

Cyberchondria, Cyberchondria Severity Scale, Health Anxiety, Hypochondriasis, Online Health Information Seeking, Cognitive-Behavioral Model, Safety Behaviors, Psychometrics, Factor Analysis, DASS-21

3. Authors

The Cyberchondria Severity Scale was developed and empirically validated by psychometricians and clinical researchers based at the School of Psychology, Faculty of Life and Health Sciences, University of Ulster, Northern Ireland, United Kingdom:

• Dr. Eoin McElroy — Lecturer and Researcher in Psychology, School of Psychology, University of Ulster. Dr. McElroy specializes in quantitative psychometrics, latent variable modeling, psychiatric epidemiology, and the psychological mechanisms mediating technology use, digital behaviors, and emotional dysregulation. Email: [email protected]
• Prof. Mark Shevlin — Professor of Psychology, School of Psychology, University of Ulster. Professor Shevlin is an internationally recognized expert in structural equation modeling, latent class analysis, psychiatric classification, psychological trauma, and the psychometric evaluation of clinical diagnostic instruments. Email: [email protected]

4. Purpose

The development of the Cyberchondria Severity Scale arose from an urgent necessity to bridge a critical methodological divide within behavioral medicine, clinical psychology, and cyberpsychology. During the early 2000s and 2010s, widespread adoption of broadband internet and commercial search engines transformed public access to biomedical databases, symptom-checker websites, and lay health forums. While this democratized access to health data, clinicians and epidemiologists observed a concurrent clinical phenomenon: for a notable subset of the population, internet searching did not lead to therapeutic empowerment, but instead triggered escalating cycles of panic, intrusive illness worries, and somatic hypervigilance. Prior to the publication of the CSS by McElroy and Shevlin in 2014, scholarly investigations into this pathology were constrained by unstandardized, ad-hoc, or single-item survey metrics that conflated cyberchondria with generalized health anxiety or non-pathological information retrieval.

The primary purpose of the CSS is therefore to provide an empirically grounded, multidimensional, psychometrically validated scale that isolates the idiosyncratic cognitive, affective, and behavioral characteristics of cyberchondria. Traditional instruments assessing hypochondriasis or illness anxiety disorder—such as the Health Anxiety Questionnaire (HAQ) or the Whiteley Index—predated the modern digital landscape. Consequently, they fail to evaluate the unique behavioral feedback loops introduced by algorithm-driven search engines, which frequently rank catastrophic, rare, or fatal pathologies above benign explanations for ambiguous physiological sensations. The CSS was engineered to capture this digital-specific phenomenology by mapping the exact parameters under which online searches cease to be adaptive and transform into compulsive safety behaviors that perpetuate psychological disability.

In research contexts, the scale serves as a standardized dependent or independent variable to explore the epidemiology, etiology, and socioeconomic burdens associated with cyberchondria. By quantifying symptom severity along a continuous scale, investigators can assess how cyberchondria correlates with healthcare overutilization, unnecessary diagnostic imaging, ambulatory care costs, ambulatory doctor-shopping, and general functional disability. Furthermore, the CSS enables longitudinal modeling of how dispositional traits—such as neuroticism, intolerance of uncertainty, and somatosensory amplification—interact with digital interface designs to foster compulsive search patterns.

Clinically, the CSS operates as a screening and diagnostic tracking instrument within outpatient mental health, primary care, and psychosomatic medicine environments. Practitioners can deploy the scale during initial intake assessments to determine whether a patient’s health anxiety is mediated or amplified by internet connectivity. Identifying elevated subscale scores allows cognitive-behavioral therapists to design individualized treatment plans. For instance, marked elevations on the Compulsion and Excessiveness subscales signal a need for behavioral exposure and response prevention (ERP) aimed at digital abstinence or restricted searching, whereas high scores on the Mistrust of Medical Professional subscale alert physicians to fragile therapeutic alliances, facilitating direct, non-defensive communication strategies that discourage antagonistic self-diagnosis.

5. Psychological Construct

Cyberchondria is operationalized within the CSS as a multidimensional construct characterized by repetitive, compulsive, and dysregulated online searching for medical and health-related information, which directly triggers or intensifies health anxiety, emotional distress, and functional impairment. Grounded in cognitive-behavioral frameworks, the condition is not conceptualized merely as a frequent internet search habit; rather, it reflects a continuous spectrum of psychopathology wherein digital searches function as maladaptive safety-seeking mechanisms that backfire, trapping the user in self-sustaining cycles of physiological hypervigilance and cognitive catastrophic misinterpretation. The CSS delineates this overarching construct into five interrelated yet structurally distinct dimensions:

Compulsion

The Compulsion dimension captures the subjective loss of volitional control regarding online health investigations and the profound behavioral urgency driving the individual to initiate and sustain symptom searching. Paralleling the symptomology of Obsessive-Compulsive Disorder (OCD), individuals scoring high on this dimension experience an irresistible impulse to consult search engines upon noticing even trivial bodily variations. They report an inability to disengage from the behavior despite recognizing its counterproductive nature, observing that initial searches for benign symptoms rapidly cascade into open tabs examining catastrophic illnesses. This dimension also measures the degree to which these compulsive search episodes disrupt daily obligations, vocational duties, social responsibilities, and sleep hygiene.

Distress

The Distress subscale measures the immediate and enduring affective disturbance evoked by consuming online biomedical content. Rather than experiencing tranquility or intellectual satisfaction, the individual reacts to digital search results with subjective fear, physiological panic, heightened anxiety, and existential dread. The retrieved information leads them to believe they are unusually susceptible to rare, debilitating, or terminal conditions. This dimension measures cognitive distortions such as catastrophic thinking and selective abstraction, wherein the searcher filters out common, benign explanations in favor of severe diagnostic conclusions, ultimately provoking insomnia, irritability, and despair.

Excessiveness

The Excessiveness dimension assesses the quantitative, temporal, and structural parameters of the search behavior. It operationalizes cyberchondria along metrics of duration, frequency, and repetition, evaluating whether the individual spends hours at a time browsing health databases, navigates dozens of disparate web pages during a single session, or conducts symptom searches on a daily basis. This dimension highlights behavioral overinvestment, capturing how the sheer volume of search activities encroaches upon leisure activities, professional routines, and interpersonal relationships.

Reassurance Seeking

The Reassurance Seeking subscale evaluates the paradoxical, self-reinforcing behavioral drive to neutralize health-related fears via online exploration. The underlying cognitive premise is that discovering confirmatory evidence of benignity will provide relief and restore peace of mind. However, because digital medical databases are saturated with ambiguous, contradictory, and worst-case scenarios, the temporary relief derived from a reassuring search is fleeting. As soon as a novel bodily sensation or an unaddressed risk factor emerges, the craving for reassurance reignites, propelling the individual into another protracted search loop. This dimension maps closely onto the behavioral maintenance cycles seen in classical hypochondriasis.

Mistrust of Medical Professional

The Mistrust of Medical Professional dimension evaluates the interpersonal fallout that occurs when online health investigations collide with formal clinical encounters. Individuals exhibiting elevations on this subscale report questioning, challenging, or dismissing their physician’s clinical judgment when the professional diagnosis contradicts their self-directed internet findings. Believing that their comprehensive personal research offers a superior or more nuanced perspective than the doctor’s physical examination, these patients experience significant skepticism toward medical providers, leading to treatment non-adherence, repeated demands for redundant medical tests, and combative doctor-patient dynamics.

6. Theoretical Framework

The conceptual foundation of the Cyberchondria Severity Scale is rooted in the cognitive-behavioral model of health anxiety, pioneeringly formulated by Paul Salkovskis and expanded by Warwick, Asmundson, and Taylor. Within the classical cognitive-behavioral model, health anxiety is initiated and maintained by catastrophic misinterpretations of normal, benign, or ambiguous bodily sensations. When an individual perceives a harmless somatic fluctuation (such as a transient headache, muscle twitch, or dermatological blemish), maladaptive core beliefs regarding illness vulnerability, personal fragility, and the catastrophic nature of disease are automatically activated. This cognitive appraisal produces autonomic arousal, which generates further somatic symptoms, reinforcing the catastrophic belief via a vicious biological and psychological feedback loop.

Crucially, cognitive-behavioral theory posits that individuals attempt to downregulate this escalating distress through the deployment of “safety behaviors.” Safety behaviors are actions executed with the explicit intent of preventing, mitigating, or verifying a feared health catastrophe. In traditional presentations of health anxiety, these behaviors manifest as physical checking (e.g., repeatedly palpating lymph nodes), reassurance-seeking from family members, or excessive medical consultations. In the digital era, McElroy and Shevlin integrated this model with modern cybernetic and information processing frameworks to explain cyberchondria:

The internet serves as an inherently flawed safety behavior mechanism due to the architecture of digital information retrieval. Search algorithms are optimized for click-through engagement, novelty, and comprehensiveness, systematically biasing results toward severe, rare, or catastrophic conditions over benign explanations—a dynamic termed “escalation of medical concerns in web search” by computer scientists Ryen White and Eric Horvitz. When an anxious individual enters an ambiguous symptom like “muscle twitching,” the algorithm may return amyotrophic lateral sclerosis (ALS) alongside fatigue. Driven by a confirmation bias and profound intolerance of uncertainty, the individual focuses exclusively on the catastrophic diagnosis, interpreting the worst-case scenario as highly probable.

Rather than providing reassurance, digital searches introduce cognitive ambiguity, contradictory medical opinions, and distressing imagery. The brief reduction in distress that occasionally follows the identification of a benign cause acts as a schedule of intermittent reinforcement, which is known to be the most potent operant conditioning schedule for sustaining behavioral habits. Consequently, the user becomes trapped in a compulsive feedback cycle: anxiety prompts searching, searching exposes the user to threatening data, anxiety escalates, and further searches are conducted to neutralize the heightened threat. The CSS was structurally engineered around these theoretical mechanisms, operationalizing the compulsive drive, affective escalation, excessive investment, and the subsequent erosion of clinical trust that arise from this cognitive-behavioral breakdown.

7. Validity

The psychometric validity of the Cyberchondria Severity Scale was rigorously evaluated during its inception and has since been corroborated across diverse clinical, non-clinical, cross-cultural, and international cohorts. The validation process encompasses construct validity, convergent and discriminant validity, and criterion-related validity.

Construct and Factorial Validity

In the foundational validation study by McElroy and Shevlin (2014), construct validity was established through exploratory factor analysis on a university-derived sample (N = 208). The retention of five distinct, theoretically coherent factors was empirically justified via Horn’s parallel analysis, which eliminated researcher subjectivity in factor extraction. Subsequent confirmatory factor analyses (CFA) conducted by researchers worldwide have consistently validated the multidimensional construct of the CSS. While some structural debates have emerged regarding the position of the Mistrust of Medical Professional subscale, the multidimensional construct remains psychometrically sound, demonstrating that the tool successfully captures the diverse operational expressions of digital health anxiety.

Convergent and Discriminant Validity

Convergent validity was evaluated by correlating CSS subscale and total scores against well-validated psychometric measures of negative affect and psychological distress, most notably the Depression, Anxiety and Stress Scale (DASS-21). The nomological network of cyberchondria posits that while the condition shares variance with generalized emotional distress, it is fundamentally an anxiety-driven phenomenon characterized by specific behavioral obsessions. The empirical data corroborated this hypothesis:

This distinct pattern demonstrates strong convergent validity with somatic and health-specific anxiety constructs, alongside discriminant validity against generalized depressive affect. Cyberchondria cannot be reduced to a mere reflection of non-specific negative affectivity; it operates as an autonomous syndrome centered on health-focused safety behaviors.

Criterion-Related and Concurrent Validity

Criterion-related validity has been demonstrated by examining objective behavioral metrics. Individuals scoring high on the CSS display significantly greater daily internet screen time allocated to health domains, frequent navigation of online disease forums, higher rates of unnecessary outpatient doctor visits, and elevated scores on somatic symptom inventories. Furthermore, the CSS demonstrates strong concurrent validity in identifying patients who meet DSM-5 diagnostic criteria for Illness Anxiety Disorder or Somatic Symptom Disorder.

8. Reliability

The Cyberchondria Severity Scale exhibits strong reliability profiles across multiple measurement modalities, including internal consistency, composite reliability, and temporal stability across independent replications.

Internal Consistency

In the initial scale construction by McElroy and Shevlin (2014), the 33 retained items demonstrated exceptional internal consistency across all latent dimensions. Rather than relying solely on global scale metrics, reliability was evaluated independently across each extracted factor using Cronbach’s alpha (α):

• Compulsion Subscale (8 items): Displays robust internal consistency, with α coefficients consistently yielding values between .85 and .92 across diverse validation samples.
• Distress Subscale (8 items): Demonstrates high internal consistency, with α values spanning .88 to .94, confirming minimal measurement error in capturing affective symptom spirals.
• Excessiveness Subscale (8 items): Achieves α coefficients ranging from .82 to .91, indicating strong internal homogeneity in measuring search volume and time investment.
• Reassurance Seeking Subscale (6 items): Demonstrates good internal consistency, with α values ranging from .77 to .86.
• Mistrust of Medical Professional Subscale (3 items): Demonstrates acceptable to good internal reliability, with α coefficients typically falling between .72 and .81, which is psychometrically robust given that the subscale comprises only three indicators.
• Full Scale Total Score (33 items): The overall scale reaches exceptional internal consistency, yielding overall Cronbach’s α values between .92 and .96 and McDonald’s omega hierarchical (ω_h) values exceeding .88, demonstrating a strong, unified underlying dimension of cyberchondria severity.

Test-Retest Reliability and Temporal Stability

Subsequent psychometric investigations evaluating the temporal stability of the CSS across longitudinal periods (varying from two-week intervals in test-retest designs to multi-month epidemiological monitoring) have reported intraclass correlation coefficients (ICC) and test-retest correlation coefficients consistently exceeding r = .80 (ranging from .78 to .88). These metrics confirm that the CSS measures a stable behavioral trait and psychological predisposition, rather than transient, day-to-day emotional fluctuations.

9. Factor Analysis

The structural elucidation of the Cyberchondria Severity Scale represents an exemplary implementation of modern psychometric protocols, designed to prevent common methodological pitfalls such as factor over-extraction and item cross-loadings.

Exploratory Factor Analysis (EFA) & Item Reduction

The authors initially generated an item pool of 43 candidate questions derived from clinical literature on hypochondriasis, obsessive-compulsive checking behaviors, and internet search psychology. This 43-item inventory was administered to an initial cohort of 208 university students. To objectively determine the number of underlying factors, the researchers implemented Horn’s Parallel Analysis (comparing empirical eigenvalues from the sample correlation matrix against eigenvalues generated from synthetic Monte Carlo random datasets with identical sample size and variable counts). This procedure indicated that five latent dimensions possessed eigenvalues exceeding random noise, advising the extraction of a correlated five-factor model.

The initial 43 items were then subjected to Exploratory Factor Analysis using oblique rotation (such as Promax or Oblimin), allowing the latent factors to correlate in alignment with theoretical expectations. Stringent, pre-established item retention criteria were enforced:

• An item was retained only if its primary factor loading was ≥ .30 (with the majority of final items exhibiting loadings > .50).
• Items displaying significant cross-loadings (i.e., substantial secondary factor loadings within .15 to .20 of the primary loading) were excised.
• Ten items failed these criteria due to factorial complexity or inadequate communalities and were deleted, leaving a clean, 33-item instrument across five correlated factors.

Confirmatory Factor Analysis (CFA) and Structural Debates

Following the initial publication, structural equation modeling specialists evaluated the scale using Confirmatory Factor Analysis across diverse demographic cohorts. CFA investigations evaluating the original 33-item, five-factor first-order model have demonstrated acceptable to good global model fit indices:

• Root Mean Square Error of Approximation (RMSEA) ≤ .05 to .07
• Comparative Fit Index (CFI) ≥ .90 to .95
• Tucker-Lewis Index (TLI) ≥ .90 to .94
• Standardized Root Mean Square Residual (SRMR) ≤ .06

In subsequent psychometric literature, several structural nuances have been explored. Prominent clinical psychometrician Thomas A. Fergus evaluated alternative structural configurations, including a bifactor model. Fergus demonstrated that a general “Cyberchondria” factor accounts for the preponderance of common item variance, validating the use of a single composite total score. However, these investigations also revealed that the Mistrust of Medical Professional subscale consistently shared the lowest loadings with the general cyberchondria latent factor, occasionally displaying divergent correlations. Consequently, psychometric consensus supports calculating subscale scores independently, and some clinical researchers advocate for analyzing the Mistrust subscale as an associated, collateral construct rather than conflating it within a global severity sum.

10. Instrument / Measurement Tool

The Cyberchondria Severity Scale is structured as an objective, self-report psychometric inventory designed for rapid administration in both paper-and-pencil formats and secure digital clinical assessment platforms.

Instrument Specifications

• Instrument Name: Cyberchondria Severity Scale (CSS)
• Test Format: Standardized self-report questionnaire
• Item Count: 33 items (derived from an initial pool of 43 candidate items)
• Response Format: 5-point Likert scale (1 = Never, 2 = Once in a while, 3 = Sometimes, 4 = Fairly Often, 5 = Always)
• Target Population: Adults and adolescents aged 18 to 60+ years
• Administration Time: Approximately 5 to 10 minutes
• Primary Dimensions / Subscales (5):
  • Compulsion (8 items): Items 1, 2, 4, 11, 14, 16, 26, 31
  • Distress (8 items): Items 6, 9, 13, 17, 18, 20, 24, 30
  • Excessiveness (8 items): Items 7, 8, 12, 19, 21, 22, 27, 32
  • Reassurance (6 items): Items 3, 10, 15, 23, 28, 29
  • Mistrust of Medical Professional (3 items): Items 5, 25, 33

Scoring Instructions & Interpretation

• Item-Level Scoring: Each item is assigned a point value matching the Likert selection:
  • 1 = Never
  • 2 = Once in a while
  • 3 = Sometimes
  • 4 = Fairly Often
  • 5 = Always
• Subscale Scores: Calculated by summing the raw scores of the constituent items for each respective subscale:
  • Compulsion: Sum of items 1, 2, 4, 11, 14, 16, 26, 31 (Score range: 8 to 40)
  • Distress: Sum of items 6, 9, 13, 17, 18, 20, 24, 30 (Score range: 8 to 40)
  • Excessiveness: Sum of items 7, 8, 12, 19, 21, 22, 27, 32 (Score range: 8 to 40)
  • Reassurance: Sum of items 3, 10, 15, 23, 28, 29 (Score range: 6 to 30)
  • Mistrust of Medical Professional: Sum of items 5, 25, 33 (Score range: 3 to 15)
• Total Score Calculation: The global cyberchondria severity score is obtained by summing the raw scores across the 33 items (Score range: 33 to 165). In some empirical and clinical protocols following bifactor recommendations, researchers utilize a 30-item core total score (excluding the 3 Mistrust items, range: 30 to 150) to reflect pure online health anxiety and search compulsivity.
• Clinical Interpretation: Higher total and subscale scores indicate greater severity of cyberchondria, marked behavioral dysfunction, and intense health-related digital distress. While no universal diagnostic cutoff exists, clinical researchers frequently categorize severity using sample percentiles (e.g., scores ≥ 75th percentile indicating moderate risk; scores ≥ 90th percentile indicating clinically severe cyberchondria requiring structured intervention).

11. Permissions & Fee and Test Year

The Cyberchondria Severity Scale was developed in 2014 and published in the peer-reviewed journal Journal of Anxiety Disorders. The copyright for the original article and scale documentation is held by Elsevier Ltd. (© 2014 Elsevier Ltd. All rights reserved; DOI: 10.1016/j.janxdis.2013.12.007).

For non-commercial academic research, student theses, and individual non-profit clinical diagnostics, the scale items and scoring algorithms are generally accessible under fair-dealing scholarly provisions. Researchers intending to incorporate the CSS into formal clinical trials, epidemiological surveys, institutional investigations, or commercial electronic medical systems should secure formal permissions via the Elsevier RightsLink® clearinghouse or by contacting the corresponding authors directly (Dr. Eoin McElroy, [email protected]; Prof. Mark Shevlin, [email protected]).

12. References

Baumgartner, S. E., & Hartmann, T. (2011). The role of health anxiety in online health information search. Cyberpsychology, Behavior, and Social Networking, 14(10), 613–618. https://doi.org/10.1089/cyber.2010.0425

Crawford, J. R., & Henry, J. D. (2003). The Depression Anxiety Stress Scales (DASS): Normative data and latent structure in a large non-clinical sample. British Journal of Clinical Psychology, 42(2), 111–131. https://doi.org/10.1348/014466503321903544

Fergus, T. A. (2014). The Cyberchondria Severity Scale (CSS): An examination of structure and relations with health anxiety in a community sample. Journal of Anxiety Disorders, 28(6), 504–510. https://doi.org/10.1016/j.janxdis.2014.05.006

Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44(2), 227–239. https://doi.org/10.1348/014466505X29657

McElroy, E., & Shevlin, M. (2014). The development and initial validation of the Cyberchondria Severity Scale (CSS). Journal of Anxiety Disorders, 28(2), 259–265. https://doi.org/10.1016/j.janxdis.2013.12.007

Muse, K., McManus, F., Leung, C., Meghreblian, B., & Williams, J. M. G. (2012). Cyberchondriasis: Fact or fiction? A preliminary examination of the relationship between health anxiety and searching for health information on the Internet. Journal of Anxiety Disorders, 26(1), 189–196. https://doi.org/10.1016/j.janxdis.2011.11.005

Salkovskis, P. M., & Warwick, H. M. C. (1986). Morbid beliefs and psychological treatment of health anxiety (hypochondriasis). Behaviour Research and Therapy, 24(5), 597–602. https://doi.org/10.1016/0005-7967(86)90041-0

Salkovskis, P. M., Rimes, K. A., Warwick, H. M. C., & Clark, D. M. (2002). The Health Anxiety Inventory: Development and validation of scales for the operational measurement of health anxiety and hypochondriasis. Psychological Medicine, 32(5), 843–853. https://doi.org/10.1017/S0033291702005822

Starcevic, V. (2013). Cyberchondria: Towards a better understanding of excessive health-related Internet use. Expert Review of Neurotherapeutics, 13(2), 205–213. https://doi.org/10.1586/ern.12.162

Starcevic, V., & Berle, D. (2013). Cyberchondria: Towards a better understanding of abnormal health-related search behavior on the Internet. World Psychiatry, 12(3), 273–274. https://doi.org/10.1002/wps.20073

White, R. W., & Horvitz, E. (2009). Cyberchondria: Studies of the escalation of medical concerns in Web search. ACM Transactions on Information Systems, 27(4), 1–37. https://doi.org/10.1145/1629096.1629101

13. Items of the Scale (Questionnaire)