Online Health Information Seeking Behavior Among Patients with Diabetes: The Mechanisms of Information Overload, Perceived Risk, and Health Anxiety
Download PDF

Keywords

Diabetic patients
Online health information seeking
Information overload
Perceived risk
Health anxiety
Mixed-methods research

DOI

10.26689/jcnr.v10i6.14971

Submitted : 2026-06-17
Accepted : 2026-07-02
Published : 2026-07-17

Abstract

Objective: To explore the characteristics of online health information seeking behavior among diabetic patients, identify its influencing factors, and reveal the underlying mechanisms, thereby providing a theoretical basis for optimizing health information service platforms and enhancing patients’ information literacy. Methods: A mixed-methods research design was employed. First, qualitative analysis through in-depth interviews (n = 15) was conducted to examine diabetic patients’ online information-seeking processes, exploring how antecedent factors such as information overload and perceived risk influence patients’ information-seeking behavior through the mediating role of health anxiety. Second, through literature analysis and theoretical derivation, an integrated model of online health information-seeking behavior among diabetic patients was constructed based on the CAC (Cognitive-Affective-Behavioral) theoretical framework. Results: The study identified four key characteristics of patients’ online information-seeking: (1) Information-seeking is multifaceted and progressive, evolving from symptom recognition to disease management needs; (2) Patients commonly face information overload dilemmas, struggling to distinguish information authenticity and quality, with significant advertising interference; (3) Perceived risks (medical pitfalls, privacy breaches) and information overload mutually reinforce each other, triggering health anxiety; (4) Health anxiety simultaneously drives active information-seeking and leads to over-reliance and decision difficulties. The proposed integrated model indicates that information environment quality, patient information literacy, and platform design are three critical factors influencing information-seeking efficacy. Conclusion: The transition of patients from passive recipients of medical guidance to active self-managers is an important global health trend; however, this transition faces structural barriers. Optimizing the online health information ecosystem, enhancing patient information literacy, and establishing information credibility assessment mechanisms are important pathways to improve patients’ information-seeking efficacy.

References

International Diabetes Federation, 2025, IDF Diabetes Atlas, 11th Edition.

Saeedi P, Petersohn I, Salpea P, et al., 2019, Global and Regional Diabetes Prevalence Estimates for 2019 and Projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th Edition. Diabetes Research and Clinical Practice, 157: 107843.

Misra S, Khunti K, Goyal A, et al., 2025, Managing Early-Onset Type 2 Diabetes in the Individual and at the Population Level. Lancet, 405(10497): 2341–2354.

Zeng R, Li Y, 2023, Meta-Analysis of the Relationship Between Self-Efficacy and Online Health Information Seeking. Advances in Psychological Science, 31(4): 535–551.

Han J, Fan W, Luo X, et al., 2018, Research Progress on the Impact of Users’ Health Information-Seeking Behavior on Health Behaviors. Information and Documentation Work, 2018(2): 48–55.

Wang M, Zhou C, Lin G, 2021, A Review of Research on the Impact of Online Health Information-Seeking Behavior. Knowledge Management Forum, 6(3): 158–166.

Ming Y, Matteson M, Zhang Y, 2026, Relationship Between Online Health Information Acquisition and Shared Decision-Making Among Patients with Diabetes: Cross-Sectional Survey Study. Journal of Medical Internet Research, 28: e86137.

Hwang H, Kim N, You J, et al., 2025, Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences with Disease Management: Topic and Sentiment Analysis. Journal of Medical Internet Research, 27: e64838.

Pengpid S, Peltzer K, 2024, Prevalence and Associated Factors of Depressive Symptoms Among Older Adults in the Philippines. Actas Esp Psiquiatr, 52(5): 705–715.

Chen J, Duan Y, Xia H, et al., 2025, Online Health Information Seeking Behavior Among Breast Cancer Patients and Survivors: A Scoping Review. BMC Women’s Health, 25(1): 1.

Sundell E, Wångdahl J, Grauman Å, 2022, Health Literacy and Digital Health Information-Seeking Behavior - A Cross-Sectional Study Among Highly Educated Swedes. BMC Public Health, 22(1): 2278.

Jia C, Li P, 2024, Generation Z’s Health Information Avoidance Behavior: Insights from Focus Group Discussions. Journal of Medical Internet Research, 26: e54107.

Wong K, Wong K, O’Brien B, et al., 2026, Reflexive Thematic Analysis. Academic Medicine, 101(6): 737–738.

Bodenstein T, Kemmerling A, Kemmerling A, et al., 2026, Taking Stock of Qualitative Methods of Evaluation: A Study of Practices and Quality Criteria. Evaluation Review, 50(1): 89–115.

Pong C, Roberts N, Lum E, 2024, The “What, Why, and How?” of Story Completion in Health Services Research: A Scoping Review. BMC Medical Research Methodology, 24(1): 159.

Kishore Kumar C, Premaraja R, Arulraja S, et al., 2026, Scope and Prospects of Social Media for Patient Education and Engagement in Medical Practice. Cureus, 18(4): e106411.

Bendig E, Bauereiß N, Ebert D, et al., 2018, Internet-Based Interventions in Chronic Somatic Disease. Deutsches Ärzteblatt International, 115(40): 659–665.

Andrade A, Di Girolamo Martins G, Scatena A, et al., 2022, The Effect of Psychosocial Interventions for Reducing Co-Occurring Symptoms of Depression and Anxiety in Individuals with Problematic Internet Use: A Systematic Review and Meta-Analysis. International Journal of Mental Health and Addiction, (online): 1–22.

Last B, Schriger S, Timon C, et al., 2021, Using Behavioral Insights to Design Implementation Strategies in Public Mental Health Settings: A Qualitative Study of Clinical Decision-Making. Implementation Science Communications, 2(1): 6.

Ou Z, Wei J, Guo Y, et al., 2026, Information Overload, Cognitive Fusion, and Health Literacy Among Individuals with Type 2 Diabetes: A Moderated Network Analysis. Scientific Reports, 16(1): 4203.

McMullan R, Berle D, Arnáez S, et al., 2019, The Relationships Between Health Anxiety, Online Health Information Seeking, and Cyberchondria: Systematic Review and Meta-Analysis. Journal of Affective Disorders, 245: 270–278.

Soroya S, Farooq A, Mahmood K, et al., 2021, From Information Seeking to Information Avoidance: Understanding the Health Information Behavior During a Global Health Crisis. Information Processing & Management, 58(2): 102440.

Karamitros G, Karamitrou I, Grant M, et al., 2025, The Intersection of Quality and Readability in Online Health Information: Implications for Patient Education—A Critical Perspective. Dermatologic Surgery, 51(6): 653–654.