Impact of Cyberchondria on Health and Quality of Life: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Cyberchondria is often associated with psychological distress, straining doctor-patient relationships, and financial burdens. Over the past few decades, increasing research has explored its associations with quality of life (QoL). However, existing reviews have not comprehensively synthesized or narratively analyzed these connections. OBJECTIVE: This study aims to consolidate current research, identify key trends, and examine how cyberchondria affects QoL, while providing insights for future research directions. METHODS: The literature search was conducted on 4 databases PsycINFO, PubMed, CINAHL, and Web of Science. The review was restricted to peer-reviewed journals published in English from inception to October 9, 2025. The inclusion criteria were as follows (1) original studies examining health-related factors associated with cyberchondria, (2) participants of any demographic, and (3) English-language full texts. Studies were excluded if they assessed health anxiety as a representation of cyberchondria. The Newcastle-Ottawa Scale for cross-sectional studies was used to assess the risk of bias in the included studies. Narrative analysis was used for data synthesis. This review was reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. RESULTS: A total of 9483 records were identified from 4 databases, with 87 studies meeting the inclusion criteria for this review. All of the included studies used a cross-sectional design. Most of the included studies were rated as moderate risk (54.4%) to low risk (36.7%). Correlations were found between cyberchondria and QoL domains, including physical health (eg, pain and discomfort, sleep quality), psychological health (eg, anxiety, fear, negative feelings or emotions, anxiety sensitivity, intolerance of uncertainty, obsessive-compulsive symptoms, and depression), level of independence (eg, usual or daily activities, and mobility), social relationship (eg, personal relationship, communication, and social support), environment (eg, eHealth literacy and financial satisfaction), and behavior (eg, addictive behavior). CONCLUSIONS: This scoping review synthesizes key risk factors and challenges influencing the QoL in individuals with cyberchondria. The findings emphasize the need for clinicians to adopt a holistic approach to assess and manage cyberchondria, addressing its multifaceted impact on QoL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".