Abstract
In the digital age, patients with type 2 diabetes mellitus (T2DM) are exposed to extensive health information, leading to information overload (IO). This overload results in decision-making difficulties, increased cognitive burden, and reduced health literacy (HL). This study investigates the complex relationship among IO, cognitive fusion (CF), and HL, emphasizing the moderating role of CF. A cross-sectional survey was conducted involving 233 patients with T2DM. Participants completed a general information questionnaire, the Information Overload Scale (IOS), the Cognitive Fusion Questionnaire (CFQ), and the Diabetes Health Literacy Scale (DHLS). A moderated network analysis was used to explore the bidirectional associations between IO and HL and to verify the moderating effect of CF. Network analysis revealed several significant bidirectional relationships between IO and HL, with CF showing a significant moderating effect. Reduced CF strengthened the positive effects of “perceived diabetes information overload” and “multi-channel diabetes information stress” on “functional health literacy”, as well as the effect of “increased diabetes information device maintenance” on “interactive health literacy”. “perceived diabetes information overload” had the highest centrality index in the network model, which demonstrated overall good stability. This study advances understanding of the relationships among IO, CF, and HL in patients with T2DM. The findings suggest that interventions aimed at alleviating cognitive rigidity should adopt comprehensive strategies targeting the IO symptom network to enhance HL in this population.
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Data and materials are available to the corresponding author upon reasonable request.
References
Yan, Y. et al. Prevalence, awareness and control of type 2 diabetes mellitus and risk factors in Chinese elderly population. BMC Public. Health. 22, 1382. https://doi.org/10.1186/s12889-022-13759-9 (2022).
Murea, M., Ma, L. & Freedman, B. I. Genetic and environmental factors associated with type 2 diabetes and diabetic vascular complications. Rev. Diabet. Stud: RDS. 9, 6–22. https://doi.org/10.1900/RDS.2012.9.6 (2012).
Tb, E. L. & Jw, H. Lifestyle factors, self-management and patient empowerment in diabetes care. Eur. J. Prev. Cardiol. https://doi.org/10.1177/2047487319885455 (2019). 26 2_suppl.
What is health literacy? | health literacy | CDC. https://www.cdc.gov/health-literacy/php/about/?CDC_AAref_Val=https://www.cdc.gov/healthliteracy/learn/index.html. Accessed 21 Aug 2025.
Kelly, A., Noctor, E., Ryan, L. & van de Ven, P. The effectiveness of a custom AI chatbot for type 2 diabetes mellitus health literacy: Development and evaluation study. J. Med. Internet Res. 27, e70131. https://doi.org/10.2196/70131 (2025).
Schillinger, D. et al. Association of health literacy with diabetes outcomes. JAMA 288, 475–482. https://doi.org/10.1001/jama.288.4.475 (2002).
Sen, A. et al. Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update. J. Pharm. Anal. 15. https://doi.org/10.1016/j.jpha.2025.101305 (2025).
Bai, X., Lian, S., Sun, X., Niu, G. & Liu, J. The relationship between information hoarding and selective exposure: The role of information overload, identity bubble reinforcement, and intolerance of uncertainty. BMC Psychol. 13, 736. https://doi.org/10.1186/s40359-025-03062-8 (2025).
Ramírez, A. S. & Arellano Carmona, K. Beyond fatalism: Information overload as a mechanism to understand health disparities. Soc. Sci. Med. 219, 11–18. https://doi.org/10.1016/j.socscimed.2018.10.006 (2018).
Crook, B., Stephens, K. K., Pastorek, A. E., Mackert, M. & Donovan, E. E. Sharing health information and influencing behavioral intentions: The role of health literacy, information overload, and the internet in the diffusion of healthy heart information. Health Commun. 31, 60–71. https://doi.org/10.1080/10410236.2014.936336 (2016).
Nutbeam, D. & Lloyd, J. E. Understanding and responding to health literacy as a social determinant of health. Annu. Rev. Public. Health. 42, 159–173. https://doi.org/10.1146/annurev-publhealth-090419-102529 (2021).
Khaleel, I. et al. Health information overload among health consumers: A scoping review. Patient Educ. Couns. 103, 15–32. https://doi.org/10.1016/j.pec.2019.08.008 (2020).
Mastin-Purcell, L., Richdale, A. L., Lawson, L. P. & Morris, E. M. J. Associations between psychological inflexibility processes, pre‐sleep arousal and sleep quality. Psychol. Psychother. 98, 606. https://doi.org/10.1111/papt.12584 (2025).
Cozza, A. et al. Effects of antidiabetic medications on the relationship between type 2 diabetes mellitus and cognitive impairment. Ageing Res. Rev. 112, 102834. https://doi.org/10.1016/j.arr.2025.102834 (2025).
Chi, H., Song, M., Zhang, J., Zhou, J. & Liu, D. Relationship between acute glucose variability and cognitive decline in type 2 diabetes: A systematic review and meta-analysis. PLOS One. 18, e0289782. https://doi.org/10.1371/journal.pone.0289782 (2023).
Tao, Z., Wang, Z., Lan, Y., Zhang, W. & Qiu, B. Socioeconomic status impacts Chinese late adolescents’ internalizing problems: Risk role of psychological insecurity and cognitive fusion. BMC Public Health. 25, 1427. https://doi.org/10.1186/s12889-025-22628-0 (2025).
Information need as trigger. And driver of information seeking: A conceptual analysis. Aslib J. Inf. Manag. 69, 2–21. https://doi.org/10.1108/AJIM-08-2016-0139 (2017).
Meyerhoff, H. S., Grinschgl, S., Papenmeier, F. & Gilbert, S. J. Individual differences in cognitive offloading: A comparison of intention offloading, pattern copy, and short-term memory capacity. Cognit Res: Princ Implic. 6, 34. https://doi.org/10.1186/s41235-021-00298-x (2021).
Borsboom, D. & Cramer, A. O. J. Network analysis: An integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol. 9, 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608 (2013).
Ruyang, L. & Hedi, Y. Wellness misinformation on social media: A systematic review using social cognitive theory. Health Commun. 1–16. https://doi.org/10.1080/10410236.2025.2555614 (2025).
Shi, J-Y. et al. Effect of a group-based acceptance and commitment therapy (ACT) intervention on self-esteem and psychological flexibility in patients with schizophrenia in remission. Schizophr Res. 255, 213–221. https://doi.org/10.1016/j.schres.2023.03.042 (2023).
Papachristou, N. et al. Network analysis of the multidimensional symptom experience of oncology. Sci. Rep. 9, 2258. https://doi.org/10.1038/s41598-018-36973-1 (2019).
Obamiro, K. & Lee, K. Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used? Patient Educ. Couns. 102. https://doi.org/10.1016/j.pec.2018.10.005 (2019).
Gillanders, D. T. et al. The development and initial validation of the cognitive fusion questionnaire. Behav. Ther. 45, 83–101. https://doi.org/10.1016/j.beth.2013.09.001 (2014).
朱冬梅 张伟, 尹卫, 刘巧艳. 基于分层模型糖尿病健康素养量表的编制. 镇江高专学报. 65–68.
Ls, E. Jr Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychol. Methods. https://doi.org/10.1037/1082-989X.12.1.1 (2007). 12.
Epskamp, S., Rhemtulla, M. & Borsboom, D. Generalized network psychometrics: Combining network and latent variable models. Psychometrika 82, 904–927. https://doi.org/10.1007/s11336-017-9557-x (2017).
Haslbeck, J. M. B. & Waldorp, L. J. Mgm: Estimating time-varying mixed graphical models in high-dimensional data. J. Stat. Softw. 93, 1–46. https://doi.org/10.18637/jss.v093.i08 (2020).
Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical Lasso. Biostatics (Oxf Engl). 9, 432–441. https://doi.org/10.1093/biostatistics/kxm045 (2008).
Bien, J., Taylor, J. & Tibshirani, R. A Lasso for hierarchical interactions. Ann. Stat. 41, 1111–1141. https://doi.org/10.1214/13-AOS1096 (2013).
Epskamp, S., Waldorp, L. J., Mõttus, R. & Borsboom, D. The Gaussian graphical model in cross-sectional and time-series data. Multivar. Behav. Res. 53, 453–480. https://doi.org/10.1080/00273171.2018.1454823 (2018).
Epskamp, S., Borsboom, D. & Fried, E. I. Estimating psychological networks and their accuracy: A tutorial paper. Behav. Res. Methods. 50, 195–212. https://doi.org/10.3758/s13428-017-0862-1 (2018).
Zhao, B-Y., Chen, M-R., Lin, R., Yan, Y-J. & Li, H. Influence of information anxiety on core competency of registered nurses: Mediating effect of digital health literacy. BMC Nurs. 23, 626. https://doi.org/10.1186/s12912-024-02275-3 (2024).
Wu, Y. et al. Linking online health information seeking to cancer information overload among Chinese cancer patients’ family members. Digit. Health 11. https://doi.org/10.1177/20552076251336308 (2025).
von Wagner, C., Knight, K., Steptoe, A. & Wardle, J. Functional health literacy and health-promoting behaviour in a national sample of British adults. J. Epidemiol. Community Health. 61, 1086–1090. https://doi.org/10.1136/jech.2006.053967 (2007).
Chen, S., Bai, Q., Zhu, J. & Liu, G. Impact of functional, communicative, critical and distributed health literacy on self-management behaviors in chronic disease patients across socioeconomic groups. BMC Public. Health. 25, 1776. https://doi.org/10.1186/s12889-025-23003-9 (2025).
Hs, H. L. Moderating effect of health literacy on the relationship between diabetes self-management education and self-care monitoring activities among individuals with type 2 diabetes mellitus. BMC Public Health 25. https://doi.org/10.1186/s12889-025-23765-2 (2025).
Xiao, F. et al. Neural mechanisms underlying intrinsic and extraneous cognitive loads in numerical inductive reasoning. Psychophysiology 62, e70129. https://doi.org/10.1111/psyp.70129 (2025).
Chung, H. K. S. et al. The validation, reliability, and measurement invariance of the cognitive fusion questionnaire in Chinese community-dwelling adults. BMC Psychol. 13, 629. https://doi.org/10.1186/s40359-025-03011-5 (2025).
Kan, W., Qu, M., Wang, Y., Zhang, X. & Xu, L. A review of type 2 diabetes mellitus and cognitive impairment. Front. Endocrinol. 16, 1624472. https://doi.org/10.3389/fendo.2025.1624472 (2025).
Lee, S. W. et al. The neural correlates of thought-action fusion in healthy adults: A functional magnetic resonance imaging study. Depress. Anxiety. 36, 732–743. https://doi.org/10.1002/da.22933 (2019).
Levin, M. E., Aller, T. B., Klimczak, K. S., Donahue, M. L. & Knudsen, F. M. Digital acceptance and commitment therapy for adults with chronic health conditions: Results from a waitlist-controlled trial. Behav. Res. Ther. 188, 104729. https://doi.org/10.1016/j.brat.2025.104729 (2025).
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The authors thank all participants who completed the questionnaire in this study. We also thank the editors and blind reviewers for their suggestions and comments on this paper.
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Ziqi Ou: Conceptualization, Methodology, Data curation, Formal analysis, Writing-original draft, Writing-Review &Editing; Jiahao Wei: Conceptualization, Methodology, Writing-original draft, Writing - Review & Editing; Yanzhi Guo: Data curation, Formal analysis, Software, Visualization, Writing-Review & Editing; Honglang Jiang: Data curation, Formal analysis; Lishi Chen: Software, Visualization, Writing-Review & Editing; Chunli Huang: Project administration, Supervision, Writing-Review & Editing.
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Ou, Z., Wei, J., Guo, Y. et al. Information overload, cognitive fusion, and health literacy among individuals with type 2 diabetes: a moderated network analysis. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34279-7
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DOI: https://doi.org/10.1038/s41598-025-34279-7


