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Introduction

Lichen planus (LP) is a chronic, inflammatory, and immune-mediated disorder that may affect the skin, nails, hair, and mucous membranes, with an estimated worldwide prevalence of 0.22–5%.¹ The traditional six “P’s” of LP, “Pruritic, Purple, Polygonal, Planar, Papules, and Plaques” describe the typical cutaneous presentation of LP as polygonal, flat-topped, violaceous papules and plaques, though there are many morphological variants.^2–4 Although etiology is often idiopathic, LP may be triggered by exogenous factors, including viral infections, such as hepatitis C,⁵ vaccines,^6–9 and pharmacological agents, such as anti-hypertensives, nonsteroidal anti-inflammatory agents, and antimalarials.¹⁰

With expanding use of biologic therapies in oncology, rheumatology, dermatology, and gastroenterology, LP and drug-induced lichenoid reactions are increasingly recognized as adverse events. These drug eruptions can complicate the long-term management of patients with chronic inflammatory diseases and necessitate therapeutic reassessment, due to the complexity of these targeted, immune-modulating treatments. Since the mechanisms underlying biologic-associated LP remain incompletely defined, this review aims to summarize current evidence on biologic-induced LP, outline implicated drug classes, and propose mechanistic models for its development.

Materials and Methods

Searches for peer-reviewed journal articles were conducted on August 12, 2025 using the PubMed/MEDLINE database with the search terms “lichen planus”, “lichenoid”, “biologics”, “drug-induced”, and “mechanism.” Additional search terms included “biological therapy”, “TNF inhibitor”, “checkpoint inhibitor”, “IL-17 inhibitor”, “IL-23 inhibitor”, “adalimumab”, “etanercept”, “infliximab”, “ustekinumab”, “secukinumab”, “dupilumab”, “nivolumab”, and “pembrolizumab.” Filters included “Case Reports”, “Clinical Study”, “Clinical Trial”, “Meta-Analysis”, “Review”, and “Systematic Review.” Articles were included if they presented example(s) of patients with lichenoid eruption following biologic therapy or discussed the phenomenon. Exclusion criteria included studies that did not mention lichenoid reactions and biologic therapy, vaccine-induced LP, and studies with discussion of biologic therapy as treatment for LP without presentation of LP after initiation of the therapeutic. Reference lists from these articles were used to find additional articles. Studies without readily obtainable full text in English were excluded (Figure 1).

Pathogenesis of Lichen Planus

The pathogenesis of LP is incompletely understood, but converging evidence supports that LP is driven by a cytotoxic immune response that induces apoptosis of basal keratinocytes at the dermoepidermal junction.^3,4 On immunohistochemistry of both mouse model and human biopsy samples, there is a predominance of CD8+ T lymphocytes in the lichenoid infiltrate, though CD4+ T cells, in particular T-helper-1 (Th1) cells, also play an important role in driving basal keratinocyte apoptosis.^4,11–13 Increased expression of intracellular adhesion molecule-1 (ICAM-1) by basal keratinocytes enhances this interaction, which is a unique feature of LP in comparison to other interface dermatoses, such as subacute cutaneous lupus erythematous and erythema multiforme.^14,15 Several cytokines are upregulated in this process, including tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), nuclear factor kappa B (NFκB), interleukin-1 (IL-1), IL-6, IL-8, and IL-10.^16,17 Apoptosis-related molecules, such as Fas/Apo-1 and Bcl-2, are also implicated.¹⁸ Plasmacytoid dendritic cells (pDCs) contribute to pathogenesis by producing type I interferons, a pathway which may link viral infections and LP through recruitment of CD8+ lymphocytes.¹⁹ Overall, LP likely reflects a breakdown of self-tolerance, with keratinocytes acting as both targets and amplifiers of immune injury. This immune pathogenesis contributes to the histopathological lichenoid tissue reaction pattern characteristic of LP.

Overview of Implicated Biologic Therapies

Biologic-associated LP and lichenoid eruptions have been reported in association with multiple therapeutic classes, most prominently programmed cell death protein-1 (PD-1)/PD-ligand 1 (PD-L1) inhibitors, such as pembrolizumab and nivolumab,^20,21 and TNF-α-inhibitors, such as infliximab, adalimumab, and etanercept.^22,23 In an observational study of 82 melanoma patients, 17% of patients developed lichenoid drug eruption associated with anti-PD-1 antibody treatment.²⁴ Biologics, such as those targeting IL-17 and IL-23 have also been associated with lichenoid reactions.²⁵ Our literature review identified 157 cases of lichenoid drug eruptions following biologic therapy with 24 distinct medications (Table 1). Clinical presentations were heterogenous, encompassing classic cutaneous LP (Figure 2), 50 cases of oral LP, 22 cases of lichen planus pemphigoides (LPP), 7 cases of nail LP (Figure 3), and 6 cases of lichen planopilaris. The wide spectrum of presentation suggests that the potential disruption of multiple inflammatory pathways may precipitate biologic-induced lichenoid reactions, highlighting the need to examine the immunologic mechanisms.

Figure 2 Multiple flat-topped violaceous papules, some coalescing into plaques, on the forearms and legs. Notes: Copyright ©2023. Reproduced from Zemlok SK, Buuh S, Brown R et al. Nivolumab-induced lichen planus responsive to dupilumab treatment in a patient with stage III C melanoma. JAAD Case Reports, Volume 38, 23–26.26

Figure 3 (A and B) A 75-year-old male with nail lichen planus secondary to imatinib use. Fingernails atrophic with pterygium on 7/10 fingernails, sparing the right thumbnail and left fourth and fifth fingernails. On dermoscopy, severe nail plate thinning and atrophy is apparent. Notes: Copyright ©2025. Reproduced from Axler E, Loesch E,Vaidya T et al. Nail lichen planus associated with imatinib mesylate. JAAD Case Reports, Volume 58, 25–28.27

Table 1 Characteristics of Reported Biologic-Associated Lichenoid Eruptions from the Literature, Summarized by Agent and Class

Proposed Mechanisms of Biologic-Induced Lichen Planus

Biologic-associated lichenoid eruptions have been reported across multiple biologic classes, suggesting that these reactions are attributable to nuanced immunological mechanisms. They appear to result from disruptions to immune homeostasis that ultimately favor cytotoxic T cell-mediated injury at the dermoepidermal junction.²⁸ Several mechanistic models emerge from the literature, including cytokine imbalances, immune pathway redirection, unmasking of antigens and epitope spreading, dysregulation of keratinocyte apoptosis pathways, and potential genetic or host susceptibility factors.

Cytokine Imbalances

One of the most widely proposed mechanisms for biologic-induced LP involves cytokine imbalance following targeted inhibition of specific inflammatory pathways. In idiopathic LP, type I interferons, especially IFN-α, play important roles in modulating keratinocyte damage.¹⁹ Conversely, TNF-α normally exerts an inhibitory effect on pDCs, limiting IFN-α production. Thus, TNF-α blockade by biologics like adalimumab and etanercept may lead to upregulation of cytokines including IFN-α, activating pDCs and cytotoxic T cells and generating an inflammatory response.^29–31 Inhibition of TNF-α has also been hypothesized to increase the expression of IL-17A and IL-23, stimulating the Th17 pathway.³²

Similar cytokine shifts have been hypothesized with IL-17 inhibitors, such as secukinumab.³³ It has been proposed that IL-17 inhibition may activate pDCs and promote the production of inflammatory cytokines such as IL-23, IL-12, and TNF-α, contributing to LP development.^32,33 Additionally, hydroxychloroquine is occasionally used to treat biologic-associated LP, because it interferes with toll-like receptor signaling and decreases proinflammatory cytokine production.³⁴

Although direct mechanistic studies remain limited, cytokine imbalance provides a strong potential mechanism for why certain biologics precipitate lichenoid reactions. Further research using serum cytokine profiling may delineate the precise immune shifts associated with each biologic class.

T Lymphocyte Immune Pathway Shifts

Another proposed mechanism for biologic-associated LP is the activation of a Th1-mediated immune response that stimulates autoreactive CD8+ T cell populations, resulting in basal keratinocyte apoptosis.²⁵ For example, the PD-1 pathway typically inhibits T cell activation, allowing malignant cells to escape the immune response. Therefore, biologic agents that inhibit PD-1, such as pembrolizumab and nivolumab, can remove the block on previously suppressed T cells, inducing T cell activation.^35,36 These inflammatory T lymphocytes may then proliferate and attack autoantigens in the skin, resulting in LP.³⁶ Furthermore, it has been proposed that the production of type I IFNs is closely associated with the recruitment of cytotoxic lymphocytes and pDCs via CXCR3 ligands, indicating a Th1-mediated immune response.^19,29,30 Furthermore, recent reports indicate that Th2 blockade by dupilumab may lead to a Th1/Th2 imbalance, shifting towards a Th1-dominated immune response that may result in dupilumab-induced LP.^37–40

Additionally, narrowband ultraviolet B (NBUVB) phototherapy has successfully been used to treat anti-PD-1-induced LP refractory to steroidal treatment.^41,42 Since LP is associated with a Th1/Th17 response,⁴³ NBUVB phototherapy may be used therapeutically to shift from a Th1/Th17 response to a Th2 milieu, promoting immunosuppression.^41,42 In addition to hydroxychloroquine’s decreased proinflammatory cytokine production, it also downregulates major histocompatibility class (MHC) II-peptide complex formation, resulting in decreased CD4+ T cell activation and a suppressed immune response.³⁴ Therefore, NBUVB phototherapy and hydroxychloroquine may be used to treat biologic-associated LP by shifting from proinflammatory immune responses to Th2-mediated immune pathways.

Antigen Unmasking & Epitope Spreading

It has also been hypothesized that biologic therapy may precipitate lichenoid eruptions by unmasking a previously suppressed inflammatory response to pre-existing autoantigens localized to specific sites in the body.^36,44–46 In anti-PD-1 therapy in particular, biologic-associated LP is proposed to be mediated by an unmasked antigen and/or a neoantigen directly created by the anti-PD-1 antibody.^46,47 The resulting lichenoid reactions have been found to contain susceptible mutated keratinocytes that specifically express PD-L1, resulting in infiltration of the dermoepidermal junction by autoreactive T lymphocytes and keratinocyte necrosis.^48,49 This suggests that the lichenoid eruption is a target effect of the PD-1/PD-L1 pathway, rather than a nonspecific hypersensitivity reaction.⁴⁸ Interestingly, biologic-associated LP may therefore serve as a biomarker for efficacy of treatment with immune checkpoint inhibitors, encouraging the resumption or continuation of treatment of patients with immunotherapy once the reaction is managed.^47,50

It is also reported that dermoepidermal junction damage in LP may expose previously immune-tolerated membrane antigens, eliciting an immune response through epitope spreading.⁵¹ This phenomenon has been associated with LPP in particular, with the blistering response occurring after the formation of lichenoid lesions.^51,52 The occurrence of rare variants of biologic-associated LP, such as LPP and lichen planopilaris, suggests that distinct antigenic targets within basement membrane components or hair follicles can be unmasked by biologic agents. Finally, it has been proposed that the oral LP associated with imatinib use may be closely correlated with the altered expression of epidermal markers.⁵³

Keratinocyte Apoptosis Dysregulation

Keratinocyte apoptosis is an essential component in the pathogenesis of idiopathic LP, mediated primarily through Fas/Fas ligand interactions and the perforin-granzyme B pathway.^18,54 Biologic therapy may be implicated in heightening keratinocyte vulnerability to immune-mediated injury. For example, the activation of cytotoxic T cells associated with biologic therapy may then enable T lymphocytes to secrete granules containing granulysin, perforin, and granzyme B, leading to keratinocyte apoptosis.³³ Additionally, keratinocyte apoptosis results in the release of pro-inflammatory cytokines, perpetuating the disease process.³³

Altered apoptotic signaling may particularly be implicated in association with receptor activator of nuclear factor κΒ ligand (RANKL)-inhibitor therapy. Under physiologic conditions, RANK-RANKL signaling supports peripheral immune tolerance by prolonging pDC survival, promoting regulatory T cell development, and eliminating autoreactive T cells through Fas-mediated apoptosis.^55,56 Inhibition of this pathway with denosumab therapy disrupts immune tolerance, rendering keratinocytes vulnerable to autoreactive T cell-driven apoptosis, which may promote lichenoid eruptions.⁵⁶

Genetic & Host Susceptibility Factors

While biologic therapy may provide an inciting stimulus for lichenoid reactions, not all patients exposed to immunotherapeutic agents develop LP, suggesting an important role for underlying host susceptibility. Idiopathic LP has been associated with certain HLA genotypes, such as HLA-DR1,¹ but whether similar HLA associations predispose to biologic-associated LP is unclear. However, it has been theorized that in certain genotypes, the effect of TNF-α inhibition can accelerate IFN-α production, pathologically activating T cells and pDCs.³⁰ Overall, biologic-associated LP likely represents an unmasking of preexisting immune vulnerability rather than a uniformly-induced pathogenic response. However, future studies involving HLA typing, immune gene sequencing, and longitudinal patient registries may aid in identifying at-risk individuals.

Implications for Clinical Practice

Recognition of biologic-associated LP has important implications for dermatologists, oncologists, rheumatologists, and other physicians prescribing these agents. Lichenoid eruptions can mimic idiopathic LP both clinically and histopathologically, necessitating a high index of suspicion in patients who develop new cutaneous, oral, or follicular lichenoid lesions while on biologic therapy.⁵⁷ A careful drug history is essential, as latency from drug initiation to eruption may vary widely, ranging from days to years.^21,57

From a management standpoint, most cases of biologic-associated LP respond to withdrawal of the offending biologic and topical or systemic corticosteroids.^57,58 Steroid-sparing agents, such as acitretin, cyclosporine, methotrexate, apremilast, hydroxychloroquine, phototherapy, and other biologic agents, such as dupilumab and rituximab are also often used to treat these cutaneous reactions.^{26,34,36,37,39,41,42,48,57–70} However, in patients with limited or well-controlled disease, continuation of the biologic with adjunctive treatment is often reasonable, especially if the biologic is effectively treating the underlying indication.^57,58 Nevertheless, more severe presentations of biologic-associated LP, as well as LPP and lichen planopilaris may often necessitate cessation of biologic therapy.

Importantly, management of lichenoid drug eruptions underscores the need for shared decision-making between physicians and patients. Counseling before initiation of biologic therapy should include discussion of potential cutaneous adverse events and strategies for early recognition. When considering withdrawal of biologic therapy, the balance of risks and benefits must be evaluated. Greater understanding of the mechanisms underlying biologic-associated LP may eventually enable risk stratification.

Research Gaps & Future Directions

Despite the growing number of case reports documenting biologic-associated LP, there remain several key gaps in our understanding of the pathogenesis and clinical implications. Primarily, the true incidence and risk factors remain undefined. Large, multicenter prospective studies are needed to establish incidence rates across different biologic classes and identify patient characteristics that may predispose to lichenoid eruptions, such as HLA genotype and autoimmune history. Additionally, functional studies using in vitro keratinocyte and pDC models, cytokine assays, and lesional transcriptomics may provide direct mechanistic evidence to clarify the immunologic mechanisms underlying biologic-associated LP.

Physicians and patients may also benefit from more standardized treatment algorithms. Optimal approaches to continuing versus withdrawing biologic therapy, the role of adjunctive treatments, and outcomes after rechallenge with the offending biologic remain unclear. Prospective studies evaluating therapeutic decisions and long-term outcomes are needed to streamline management strategies. Finally, studies evaluating HLA typing, immune profiling, and/or genetic testing may guide risk stratification, enabling a more individualized approach to prescribing and surveilling patients initiating biologic therapy.

Conclusion

In sum, biologic therapies are increasingly recognized as potential triggers of LP and related lichenoid eruptions. Evidence to date suggests that biologic-associated LP arise through multiple related mechanisms, including cytokine imbalance, T cell dysregulation, antigen unmasking, keratinocyte apoptosis, and host susceptibility. It is important for physicians to counsel patients regarding potential risks of biologic therapy, and carefully weigh the benefits of continuing vs withdrawing treatment. Future studies aimed at clarifying mechanisms and defining incidence, risk factors, and predictive biomarkers may improve our understanding and help guide patient care.

Abbreviations

LP, lichen planus; Th, T-helper; ICAM-1, intracellular adhesion molecule-1; TNF-α, tumor necrosis factor-α; IFN-γ, interferon-γ; NFκB, nuclear factor kappa B; IL, interleukin; pDC, plasmacytoid dendritic cell; PD-1, programmed cell death protein-1; PD-L1, programmed cell death protein-ligand 1; LPP, lichen planus pemphigoides; NBUVB, narrowband ultraviolet B; MHC, major histocompatibility complex; RANKL, receptor activator of nuclear factor κΒ ligand.

Data Sharing Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contributions

Ms. Podolsky contributed to formal analysis, investigation, methodology, visualization, and writing – original draft. Dr. Lipner contributed to conceptualization, project administration, supervision, and writing – review and editing. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

No funding was obtained for this study.

Disclosure

The authors report no conflicts of interest in this work. Dr. Lipner has served as a consultant for Moberg Pharmaceuticals and BelleTorus Corporation. AI was not used in article composition.

References

1. Gorouhi F, Davari P, Fazel N. Cutaneous and mucosal lichen planus: a comprehensive review of clinical subtypes, risk factors, diagnosis, and prognosis. ScientificWorldJournal. 2014;2014:742826. doi:10.1155/2014/742826

2. Usatine RP, Tinitigan M. Diagnosis and treatment of lichen planus. Am Fam Physician. 2011;84(1):53–60.

3. Gupta MK, Lipner SR. Review of Nail Lichen Planus: epidemiology, Pathogenesis, Diagnosis, and Treatment. Dermatol Clin. 2021;39(2):221–230. doi:10.1016/j.det.2020.12.002

4. Lehman JS, Tollefson MM, Gibson LE. Lichen planus. Int J Dermatol. 2009;48(7):682–694. doi:10.1111/j.1365-4632.2009.04062.x

5. Daoud MS, Gibson LE, Daoud S, el-Azhary RA. Chronic hepatitis C and skin diseases: a review. Mayo Clin Proc. 1995;70(6):559–564. doi:10.4065/70.6.559

6. Agrawal A, Shenoi SD. Lichen planus secondary to hepatitis B vaccination. Indian J Dermatol Venereol Leprol. 2004;70(4):234–235.

7. Hardy C, Glass J. Linear Lichen Planus in the Setting of Annual Vaccination. Mil Med. 2019;184(5–6):e467–e469. doi:10.1093/milmed/usy234

8. Usman A, Kimyai-Asadi A, Stiller MJ, Alam M. Lichenoid eruption following hepatitis B vaccination: first North American case report. Pediatr Dermatol. 2001;18(2):123–126. doi:10.1046/j.1525-1470.2001.018002123.x

9. D’Agostino M, Martora F, Megna M, Napolitano M, Potestio L. Lichen planus following COVID-19 vaccination: a narrative review. Clin Exp Dermatol. 2025;50(2):260–266. doi:10.1093/ced/llae356

10. Thompson DF, Skaehill PA. Drug-induced lichen planus. Pharmacotherapy. 1994;14(5):561–571. doi:10.1002/j.1875-9114.1994.tb02852.x

11. Shiohara T, Mizukawa Y. The immunological basis of lichenoid tissue reaction. Autoimmun Rev. 2005;4(4):236–241. doi:10.1016/j.autrev.2004.11.005

12. Mizukawa Y, Yamazaki Y, Teraki Y, et al. Direct evidence for interferon-gamma production by effector-memory-type intraepidermal T cells residing at an effector site of immunopathology in fixed drug eruption. Am J Pathol. 2002;161(4):1337–1347. doi:10.1016/s0002-9440(10)64410-0

13. Zhou S, Zhang Z, Feng X, Zhao C, Jiang L. Lichenoid mucocutaneous reactions associated with sintilimab therapy in a non-small cell lung adenocarcinoma patient: case report and review. Front Pharmacol. 2023;14:1276788. doi:10.3389/fphar.2023.1276788

14. Norris DA. Cytokine modulation of adhesion molecules in the regulation of immunologic cytotoxicity of epidermal targets. J Invest Dermatol. 1990;95(6 Suppl):111s–120s. doi:10.1111/1523-1747.ep12874977

15. Bennion SD, Middleton MH, David-Bajar KM, Brice S, Norris DA. In three types of interface dermatitis, different patterns of expression of intercellular adhesion molecule-1 (ICAM-1) indicate different triggers of disease. J Invest Dermatol. 1995;105(1 Suppl):71s–79s. doi:10.1111/1523-1747.ep12316107

16. El-Howati A, Thornhill MH, Colley HE, Murdoch C. Immune mechanisms in oral lichen planus. Oral Dis. 2023;29(4):1400–1415. doi:10.1111/odi.14142

17. Lu R, Zhang J, Sun W, Du G, Zhou G. Inflammation-related cytokines in oral lichen planus: an overview. J Oral Pathol Med. 2015;44(1):1–14. doi:10.1111/jop.12142

18. Sklavounou-Andrikopoulou A, Chrysomali E, Iakovou M, Garinis GA, Karameris A. Elevated serum levels of the apoptosis related molecules TNF-alpha, Fas/Apo-1 and Bcl-2 in oral lichen planus. J Oral Pathol Med. 2004;33(7):386–390. doi:10.1111/j.1600-0714.2004.00221.x

19. Wenzel J, Scheler M, Proelss J, Bieber T, Tüting T. Type I interferon-associated cytotoxic inflammation in lichen planus. J Cutan Pathol. 2006;33(10):672–678. doi:10.1111/j.1600-0560.2006.00527.x

20. Alharbi A, Khobrani A, Noor A, et al. Risk of Lichen Sclerosus and Lichen Planus in Patients Receiving Immune Checkpoint Inhibitors. Int J Environ Res Public Health. 2022;20(1):580. doi:10.3390/ijerph20010580

21. Le TK, Brown I, Goldberg R, et al. Cutaneous Toxicities Associated with Immune Checkpoint Inhibitors: an Observational, Pharmacovigilance Study. J Invest Dermatol. 2022;142(11):2896–2908.e4. doi:10.1016/j.jid.2022.04.020

22. Succaria F, Bhawan J. Cutaneous side-effects of biologics in immune-mediated disorders: a histopathological perspective. J Dermatol. 2017;44(3):243–250. doi:10.1111/1346-8138.13762

23. Behrangi E, Moodi F, Jafarzadeh A, Goodarzi A. Paradoxical and bimodal immune-mediated dermatological side effects of TNF-α inhibitors: a comprehensive review. Skin Res Technol. 2024;30(5):e13718. doi:10.1111/srt.13718

24. Hwang SJ, Carlos G, Wakade D, et al. Cutaneous adverse events (AEs) of anti-programmed cell death (PD)-1 therapy in patients with metastatic melanoma: a single-institution cohort. J Am Acad Dermatol. 2016;74(3):455–61.e1. doi:10.1016/j.jaad.2015.10.029

25. Kim PJ, Sachdeva M, Mufti A, Lytvyn Y, Maliyar K, Yeung J. Lichenoid Drug Eruptions Associated With the Use of Biologic Therapy: a Systematic Review. J Cutan Med Surg Sep-Oct. 2022;26(5):521–522. doi:10.1177/12034754221100188

26. Zemlok SK, Buuh S, Brown R, Murphy M, Hegde UP, Mallett JR. Nivolumab-induced lichen planus responsive to dupilumab treatment in a patient with stage III C melanoma. JAAD Case Rep. 2023;38:23–26. doi:10.1016/j.jdcr.2023.05.036

27. Axler E, Loesch E, Vaidya T, Neubauer Z, Lipner SR. Nail lichen planus associated with imatinib mesylate. JAAD Case Rep. 2025;58:25–28. doi:10.1016/j.jdcr.2024.12.039

28. Watanabe T, Yamaguchi Y. Cutaneous manifestations associated with immune checkpoint inhibitors. Front Immunol. 2023;14:1071983. doi:10.3389/fimmu.2023.1071983

29. Asarch A, Gottlieb AB, Lee J, et al. Lichen planus-like eruptions: an emerging side effect of tumor necrosis factor-alpha antagonists. J Am Acad Dermatol. 2009;61(1):104–111. doi:10.1016/j.jaad.2008.09.032

30. Jayasekera PS, Walsh ML, Hurrell D, Parslew RA. Case Report of Lichen Planopilaris Occurring in a Pediatric Patient Receiving a Tumor Necrosis Factor α Inhibitor and a Review of the Literature. Pediatr Dermatol. 2016;33(2):e143–6. doi:10.1111/pde.12768

31. Barrientos N, García-Sánchez S, Domínguez JD. Letter: lichenoid eruption induced by etanercept. Dermatol Online J. 2012;18(7):15. doi:10.5070/D32FW0W71N

32. Liu V, Moiin A, Diaz C. Lichen planus associated with secukinumab treatment for plaque psoriasis. Dermatol Online J. 2023;29(2):60779. doi:10.5070/d329260779

33. Heidari A, Fathabadi F, Ghanavati K, Rasouli A, Soleimani S, Heidari N. A systematic review of the role of interleukin inhibitors in lichen planus: therapeutic and paradoxical effects. Inflammopharmacology. 2025. doi:10.1007/s10787-025-01853-4

34. Alnajjar NF, Tahan SR, Iriarte C. Immunotherapy-associated bullous and oral erosive lichenoid eruption successfully treated with hydroxychloroquine. JAAD Case Rep. 2025;56:63–66. doi:10.1016/j.jdcr.2024.11.010

35. Martos-Cabrera L, Lladó I, Fernández-Rico P, Butrón-Bris B, Rodríguez-Jiménez P. Umbilical lichen planus induced by nivolumab. An Bras Dermatol. 2023;98(5):712–714. doi:10.1016/j.abd.2021.09.022

36. Coscarart A, Martel J, Lee MP, Wang AR. Pembrolizumab-induced pseudoepitheliomatous eruption consistent with hypertrophic lichen planus. J Cutan Pathol. 2020;47(3):275–279. doi:10.1111/cup.13587

37. Kern L, Kleinheinrich L, Feldmann R, Sator P, Stella A, Breier F. Dupilumab-Induced Lichen Planus: a Case with Oral and Cutaneous Eruptions. Case Rep Dermatol. 2022;14(3):356–361. doi:10.1159/000527918

38. Kim TE, Shin MK. De novo case of lichenoid eruption following dupilumab treatment. JAAD Case Rep. 2021;13:71–72. doi:10.1016/j.jdcr.2021.04.035

39. Gajraj FA, Zahir J, Adereti C, Gajraj MH. A Case Report and Literature Review of the Role of Dupilumab in the Management of Lichen Planus: cause or Treatment? Cureus. 2023;15(7):e41274. doi:10.7759/cureus.41274

40. Srivatsa A, Yaldo M, Rehman W, et al. What is the Verdict? A Systematic Review of Dupilumab and Lichen Planus. Int J Dermatol. 2025. doi:10.1111/ijd.17909

41. Donaldson M, Owen JL, Chae YK, Choi JN. Management of Persistent Pruritus and Lichenoid Reaction Secondary to Nivolumab With Narrowband Ultraviolet B Phototherapy. Front Oncol. 2018;8:405. doi:10.3389/fonc.2018.00405

42. Esfahani K, Henderson Berg MH, Zargham H, et al. Narrowband ultraviolet B therapy for refractory immune-related lichenoid dermatitis on PD-1 therapy: a case report. J Immunother Cancer. 2021;9(3):e001831. doi:10.1136/jitc-2020-001831

43. Solimani F, Pollmann R, Schmidt T, et al. Therapeutic Targeting of Th17/Tc17 Cells Leads to Clinical Improvement of Lichen Planus. Front Immunol. 2019;10:1808. doi:10.3389/fimmu.2019.01808

44. Edwards C, Fearfield L. Nivolumab-induced lichenoid dermatitis occurring in a patient with metastatic melanoma successfully treated with alitretinoin. Clin Exp Dermatol. 2018;43(5):609–610. doi:10.1111/ced.13410

45. Uthayakumar AK, Rudd E, Noy M, Weir J, Larkin J, Fearfield L. Severe progressive scarring pembrolizumab-induced lichen planopilaris in a patient with metastatic melanoma. Australas J Dermatol. 2021;62(3):403–406. doi:10.1111/ajd.13660

46. Shi VJ, Rodic N, Gettinger S, et al. Clinical and Histologic Features of Lichenoid Mucocutaneous Eruptions Due to Anti-Programmed Cell Death 1 and Anti-Programmed Cell Death Ligand 1 Immunotherapy. JAMA Dermatol. 2016;152(10):1128–1136. doi:10.1001/jamadermatol.2016.2226

47. Sehbai A, Hamid MA, Ibrahim Z. Pembrolizumab-Induced Lichen Planus in a Patient With Non-Small-Cell Lung Carcinoma (NSCLC) That Correlates to Therapeutic Response. Cureus. 2022;14(11):e31454. doi:10.7759/cureus.31454

48. Bansal A, Singla A, Paul D, Kaur S. Pembrolizumab-Induced Lichen Planus: a Rare Immune-Related Adverse Side Effect. Indian Dermatol Online J. 2023;14(3):391–394. doi:10.4103/idoj.idoj_377_22

49. Tembunde Y, Dika MN. Pembrolizumab-Induced Eruptive Keratoacanthomas and Lichen Planus in a Lung Cancer Patient. Cureus. 2023;15(8):e43402. doi:10.7759/cureus.43402

50. Kwon CW, Murthy RK, Kudchadkar R, Stoff BK. Pembrolizumab-induced lichen planus pemphigoides in a patient with metastatic Merkel cell carcinoma. JAAD Case Rep. 2020;6(10):1045–1047. doi:10.1016/j.jdcr.2020.03.007

51. Buján Bonino C, López-Pardo Rico M, Moreiras Arias N, Suárez Peñaranda JM, Casas Fernández L. Checkpoint inhibitor-induced lichen planus pemphigoides: a case report and literature review of an unusually reported entity. Int J Dermatol. 2023;62(4):e200–e204. doi:10.1111/ijd.16502

52. Hübner F, Langan EA, Recke A. Lichen Planus Pemphigoides: from Lichenoid Inflammation to Autoantibody-Mediated Blistering. Front Immunol. 2019;10:1389. doi:10.3389/fimmu.2019.01389

53. Ena P, Chiarolini F, Siddi GM, Cossu A. Oral lichenoid eruption secondary to imatinib (Glivec). J DermatolTreat. 2004;15(4):253–255. doi:10.1080/09546630410015556

54. Kastelan M, Prpić Massari L, Gruber F, et al. The role of perforin-mediated apoptosis in lichen planus lesions. Arch Dermatol Res. 2004;296(5):226–230. doi:10.1007/s00403-004-0512-1

55. Walsh MC, Choi Y. Regulation of T cell-associated tissues and T cell activation by RANKL-RANK-OPG. J Bone Miner Metab. 2021;39(1):54–63. doi:10.1007/s00774-020-01178-y

56. Dourra M, Mussad S, Qiblawi S, Singer R. Denosumab-induced alopecia areata with lichenoid eruption. JAAD Case Rep. 2021;17:9–11. doi:10.1016/j.jdcr.2021.09.003

57. Pach J, Leventhal JS. Cutaneous Immune-Related Adverse Events Secondary to Immune Checkpoint Inhibitors and Their Management. Crit Rev Immunol. 2022;42(4):1–20. doi:10.1615/CritRevImmunol.2023046895

58. Bhardwaj M, Chiu MN, Pilkhwal Sah S. Adverse cutaneous toxicities by PD-1/PD-L1 immune checkpoint inhibitors: pathogenesis, treatment, and surveillance. Cutan Ocul Toxicol. 2022;41(1):73–90. doi:10.1080/15569527.2022.2034842

59. Tirnanić T, Tiodorović D, Vidović N, et al. Pembrolizumab-induced lichen planus in patients with metastatic melanoma: a report of two cases and prognostic implications of cutaneous immune-related adverse events. Acta Dermatovenerol Alp Pannonica Adriat. 2023;32(3):119–122.

60. Lim O, Maher E, Miller DD. PD-1 Inhibitor Induced Hypertrophic Lichen Planus: a Case Report. Drugs R D. 2024;24(2):353–357. doi:10.1007/s40268-024-00461-x

61. Leme HJ, Ramos J, Magarreiro-Silva A, Gouveia A, Alves J. Nivolumab-Induced Lichenoid Eruption: a Case Report. Cureus. 2025;17(6):e86862. doi:10.7759/cureus.86862

62. Fixsen E, Patel J, Selim MA, Kheterpal M. Resolution of Pembrolizumab-Associated Steroid-Refractory Lichenoid Dermatitis with Cyclosporine. Oncologist. 2019;24(3):e103–e105. doi:10.1634/theoncologist.2018-0531

63. Kou L, Agarwal S, Miceli A, Kolb L, Krishnamurthy K, Schmieder S. Steroid-Refractory Lichenoid Eruption Associated with Pembrolizumab in a Patient with Non-Small Cell Lung Cancer. HCA Healthc J Med. 2021;2(6):397–400. doi:10.36518/2689-0216.1198

64. Mueller KA, Cordisco MR, Scott GA, Plovanich ME. A case of severe nivolumab-induced lichen planus pemphigoides in a child with metastatic spitzoid melanoma. Pediatr Dermatol. 2023;40(1):154–156. doi:10.1111/pde.15097

65. Varma A, Friedlander P, De Moll EH, Desman G, Levitt J. Resolution of pembrolizumab-associated lichenoid dermatitis with a single dose of methotrexate. Dermatol Online J. 2020;26(5). doi:10.5070/D3265048774

66. Kost Y, Mattis D, Muskat A, Amin B, McLellan B. Immune Checkpoint Inhibitor-Induced Psoriasiform, Spongiotic, and Lichenoid Dermatitis: a Novel Clinicopathological Pattern. Cureus. 2022;14(8):e28010. doi:10.7759/cureus.28010

67. Headd VA, Mathien A, Pei S, Kuraitis D. Nivolumab-induced lichenoid penile ulceration and re-emergent mucocutaneous eruption treated with hydroxychloroquine. Australas J Dermatol. 2024;65(4):e97–e99. doi:10.1111/ajd.14223

68. Park JJ, Park E, Damsky WE, Vesely MD. Pembrolizumab-induced lichenoid dermatitis treated with dupilumab. JAAD Case Rep. 2023;37:13–15. doi:10.1016/j.jdcr.2023.05.004

69. Brennan M, Baldissano M, King L, Gaspari AA. Successful Use of Rituximab and Intravenous Gamma Globulin to Treat Checkpoint Inhibitor- Induced Severe Lichen Planus Pemphigoides. Skinmed. 2020;18(4):246–249.

70. Geisler AN, Phillips GS, Barrios DM, et al. Immune checkpoint inhibitor-related dermatologic adverse events. J Am Acad Dermatol. 2020;83(5):1255–1268. doi:10.1016/j.jaad.2020.03.132

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Introduction

Diabetic retinopathy (DR), an ocular microvascular complication of diabetes, is a leading cause of visual impairment and blindness among working-age individuals.¹ An estimated 103 million individuals are currently living with DR worldwide, and this number is projected to rise to 160 million by 2045, driven by an aging population and improved survival rates among individuals with diabetes.² Between 1990 and 2015, DR-related blindness increased from 200,000 to 400,000 cases and visual impairment from 1.4 million to 2.6 million, reflecting a steady increase in global prevalence that poses a critical public health challenge.³ Treatment decisions for DR are often complex, as patients face a variety of options, including retinal laser photocoagulation, anti-vascular endothelial growth factor (anti-VEGF) drugs, corticosteroids, and pars plana vitrectomy.⁴ These treatment options differ in their efficacy, risks, economic burden, and impact on daily life.⁵ Improper decision-making may lead to severe consequences, including significant vision loss, irreversible blindness, and other serious secondary complications.^5,6 Evaluating these complex, high-stakes trade-offs presents significant challenges to decision-making for patients with DR.

The World Health Organization (WHO) advocates for patient involvement in clinical decision-making to uphold patients’ rights to participate in their treatment plans and maximize treatment benefits.⁷ Previous studies have demonstrated that patient participation in treatment decision-making can not only improve treatment adherence and reduce medical visits but also enhance the doctor-patient relationship and improve health outcomes.^8,9 Therefore, involving DR patients and incorporating their preferences is essential. However, little is known about patient involvement in DR treatment decisions, especially in the context of the Chinese healthcare system. Compared to many Western countries, Chinese medical culture has traditionally been characterized by a physician-centered model, in which physicians are regarded as authorities and patients often adopt a passive role, with compliance considered optimal.¹⁰ Thus, a systematic exploration of the factors influencing patient involvement in DR decision-making in China is warranted to enhance clinical decision quality.

Previous studies suggest that factors such as age, gender, economic status, educational level, health literacy, and social support may influence participation in decision-making.^11,12 However, several factors remain controversial. Two studies found that older patients tended to play a passive role in treatment decision-making,^13,14 whereas one study reported no significant age-related effects.¹⁵ One study found that female patients, compared to men, reported a greater preference for a collaborative role and a lesser preference for a passive role in decision-making,¹⁶ while another study demonstrated the opposite conclusion.¹⁷ A study indicated that high social support was associated with increased patient participation in surgical decision-making.¹⁸ In contrast, a qualitative study revealed that family support, a component of social support, sometimes hindered patient involvement and, in some cases, led to family members making decisions on the patient’s behalf.¹⁹ These contradictory findings underscore the necessity for a more systematic and theoretically grounded approach to understanding patient involvement.

To fully capture the influencing factors, a comprehensive theoretical framework is essential. The Capability, Opportunity, Motivation – Behavior (COM-B) model offers such a framework.²⁰ Widely recognized for its comprehensive and systematic approach, the COM-B model has been extensively applied to understand a range of patient health behaviors.^21–23 The model posits that an individual’s behavior is a result of their capability, opportunity, and motivation, with capability and opportunity also affecting behavior both directly and indirectly through their impact on motivation.²⁰

Through literature review and group discussions, we identified a set of potential determinants which were then mapped onto the COM-B framework. Capability is defined as an individual’s psychological and physical ability to perform a behavior. Health literacy reflects the patient’s ability to access, comprehend, and utilize health information.²⁴ This ability allows patients to understand their treatment options, evaluate risks and benefits, and communicate their preferences meaningfully. In this study, capability was conceptualized as health literacy. Opportunity encompasses physical and social factors that facilitate or prompt a behavior. After seeking medical attention, physicians often serve as the primary source of medical information for patients in China.²⁵ Research has shown that physician support is a crucial facilitator for patient involvement in treatment decisions.²⁶ Furthermore, social support from interpersonal networks, including family members and friends, can enhance patients’ psychological resilience and mitigate decision-making pressure.²⁷ For this study, we conceptualized ophthalmologist facilitation of patient involvement and social support as opportunity. Motivation refers to the internal brain processes that energize and direct behavior, including both reflective and automatic mechanisms. Patients with higher decision self-efficacy are more likely to seek information, express their preferences, and play a more engaged role in the treatment process.²⁸ Additionally, the need for decision-making involvement reflects patients’ intrinsic desire or preference for participation. In this study, we measured motivation through decision self-efficacy and the need for decision-making involvement. Figure 1 illustrates the conceptual framework that guided our study.

Given that constructs such as health literacy, social support, and need for decision-making involvement are complex and multifaceted, we conducted our analysis on their respective sub-dimensions. For example, we analyzed the functional, communicative, and critical sub-dimensions of health literacy. This approach enabled us to more comprehensively understand how different layers of capability, opportunity, and motivation collectively influenced patient decision-making behavior. The aims of this study were to investigate the current status of actual involvement roles in treatment decision-making among patients with DR and to analyze the influencing factors. The research results will provide a valuable reference for developing measures to promote patient involvement in decision-making.

Method

Study Design and Participants

This cross-sectional study was conducted at the ophthalmology center of a large public hospital in Shanghai, China, from August 2024 to January 2025. The institution serves as a regional referral center for a broad geographic area encompassing Shanghai municipality and the surrounding provinces of Jiangsu, Zhejiang, and Anhui. The study participants were recruited using a convenience sampling method. Participants meeting the following criteria were included: (1) age 18 years and above; (2) diagnosed with DR stages III to VI; (3) voluntary participation in the study and signed informed consent. Patients with mental illness, intellectual disability, or verbal communication disorders, as well as severe cardiac, hepatic, or renal dysfunction, respiratory failure, or critical illness, were excluded from the study. According to the Kendall sample size estimation method, which is calculated based on the principle that the sample size should be at least 5 to 10 times the number of variables.²⁹ Through a literature review, this study included a total of 24 predictive influencing variables, comprising 13 sociodemographic and disease characteristics and 11 variables from five scales. Considering a 10% attrition rate, the calculated total sample size of this study ranged from 134 to 267 cases. Ultimately, the study obtained 336 valid samples. This study was reported using the STROBE guidelines.

Ethical Considerations

This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval was granted by the Ethics Review Committee of Shanghai General Hospital (Approval No. 2024–098). All participants provided written informed consent and retained the right to withdraw at any time. All data were collected and analyzed anonymously to guarantee confidentiality.

Measurements

Sociodemographic and Clinical Information

The questionnaire was designed based on a literature review and consultation with ophthalmology experts. It includes the following information: gender, age, marital status, educational level, monthly per capita household income, method of healthcare payment, duration of disease diagnosis, DR stage, and comorbidities.

Control Preference Scale (CPS)

The CPS is used to assess actual roles in treatment decision-making among patients with DR. The scale was originally developed by Degner and later adapted and revised by Nolan.³⁰ Chinese scholar Xu Xiaolin and colleagues translated and revised the scale, and the Cronbach’s α coefficient of the Chinese version is 0.899.³¹ The CPS is a unidimensional scale consisting of five options to characterize the types of patient involvement in treatment decision-making. Options 1 and 2 represent the active type, option 3 represents the collaborative type, and options 4 and 5 represent the passive type.

All Aspects of Health Literacy Scale (AAHLS)

The AAHLS is used to assess patients’ health literacy levels. The scale was developed by Chinn in 2013,²⁴ and translated and revised by Wu in 2016.³² It consists of 11 items across three dimensions: functional health literacy, communicative health literacy, and critical health literacy. Each item is scored on a 3-point Likert scale (1 = rarely, 2 = sometimes, 3 = often). The total score ranges from 11 to 33, with higher values indicating greater health literacy. In this study, the Cronbach’s α coefficient of the scale was 0.834.

Social Support Rating Scale (SSRS)

The SSRS, developed by Xiao, is used to assess patients’ social support levels.³³ It includes 10 items across three dimensions: objective support, subjective support, and utilization of social support. The total score ranges from 8 to 44, with higher scores indicating higher social support. In this study, the Cronbach’s α coefficient of the scale was 0.800.

Facilitation of Patient Involvement Scale (FPIS)

The FPIS is used to measure the extent to which patients perceive that their healthcare professionals involve them in their healthcare, developed by Martin.³⁴ The Chinese version was translated and revised by Wu in 2015.³² It is a unidimensional scale with nine items. Each item is scored on a 6-point Likert scale ranging from 1 (never) to 6 (always). The scale yields a total score ranging from 9 to 54, with higher values reflecting greater healthcare providers’ facilitation of patient involvement in treatment decisions. In this study, the Cronbach’s α coefficient of the scale was 0.830.

Decision Self-Efficacy Scale (DSES)

The DSES is used to assess patients’ confidence in making treatment decisions for themselves and was developed by O’Conner.³⁵ It is a unidimensional scale comprising eleven items, each rated on a 5-point Likert scale from 0 (not at all confident) to 4 (very confident). The total score is calculated by averaging the sum of 11 items and then multiplying by 25 to convert it to a 0–100 scale. Higher scores reflect greater patient self-efficacy in treatment decision-making. In this study, the Cronbach’s α coefficient of the scale was 0.864.

Patient Expectation for Participation in Medical Decision‐Making Scale (PEPMDS)

The PEPMDS, developed by Xu, is used to assess patients’ need for participation in treatment decisions.³⁶ This scale consists of 12 items in three dimensions: need for information, need for deliberation, and need for decisional control. Items are rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The total score ranges from 12 to 60, with higher scores indicating greater patient need for involvement in treatment decisions. In this study, the Cronbach’s α coefficient of the scale was 0.846.

Data Collection

Before conducting the questionnaire survey, all research team members received standardized training to ensure the consistency and uniformity of terminology and procedures in the survey. During the distribution of the questionnaires, researchers provided participants who met the inclusion criteria with a detailed explanation of the study’s purpose, content, and procedures, and obtained their written informed consent. Participants’ medical information was collected via the hospital’s electronic medical record system. Questionnaires were distributed and collected on-site, with immediate checks to ensure the completeness and accuracy of data collection. For participants who were unable to complete the questionnaire independently due to visual impairment, the researchers administered the questionnaire via dictation based on their verbal responses.

Statistical Analysis

Data were entered independently by two investigators using EpiData 3.1 and exported to SPSS 26.0 for analysis after verification. Descriptive analyses were performed for all included variables. Frequencies and percentages were used for categorical variables, means and standard deviations (M±SD) were used for normally distributed continuous variables, and medians and interquartile ranges (IQR) were used for non-normally distributed continuous variables. The χ² test was used to analyze the differences in the types of patient involvement in treatment decision-making between categorical variables. One-way ANOVA or the non-parametric Kruskal–Wallis H-test was used to analyze continuous variables. Variables with a P<0.05 in univariate analysis were subsequently included in the unordered multinomial logistic regression analysis to determine independent factors associated with the types of patient involvement in treatment decision-making. The Variance Inflation Factor (VIF) was used to analyze whether the variables in the model have multicollinearity. A two-sided test was used, and a P-value <0.05 was considered statistically significant.

Results

Participant Characteristics

A total of 362 questionnaires were distributed in this study. Following the exclusion of 26 unqualified questionnaires, 336 valid questionnaires were collected, yielding an effective response rate of 92.8%. The mean age of the participants was 53.74±13.01 years. Among the participants, 52.1% were male, and 40.2% had attained a junior high school education or below. The largest proportion of patients was diagnosed with DR Stage IV, accounting for 47.3%. Patients who received retinal photocoagulation accounted for the largest treatment group, at 30.0%. Furthermore, 56.5% of patients presented with ocular comorbidities, and 62.5% had systemic comorbidities. The sociodemographic and clinical characteristics of the patients are shown in Table 1.

Table 1 Comparison of Sociodemographic and Disease Characteristics Among Patients with Diabetic Retinopathy in Different Decision-Making Involvement Roles (N = 336)

Descriptive Statistics of Patient Involvement in Treatment Decision-Making for Diabetic Retinopathy

Regarding actual patient involvement in treatment decision-making roles, 21.1% of patients reported being active, 30.7% reported being collaborative, and the largest proportion, 48.2%, reported being passive.

Univariate Analysis

The mean scores for the 336 patients with DR were AAHLS (24.84±3.53), SSRS (43.59±5.51), FPIS (40.39±4.78), DSES (72.03±11.44), and PEPMDS (47.89±5.39). Univariate analysis revealed significant differences in actual involvement in decision-making roles among patients with DR across several factors, including: age, educational level, monthly per capita household income, health literacy, social support, ophthalmologist facilitation of patient involvement, decision self-efficacy, and need for decision-making involvement (P<0.05). See Table 1 and Table 2.

Table 2 Comparison of COM-B Factors Among Patients with Diabetic Retinopathy in Different Decision-Making Involvement Roles (N = 336)

Multinomial Logistic Regression Analysis

An unordered multinomial logistic regression analysis was conducted to identify the independent factors influencing patient involvement in treatment decision-making for diabetic retinopathy. Prior to conducting the multinomial logistic regression, multicollinearity was assessed among all independent variables. The aggregate variables social support and need for decision-making involvement demonstrated perfect collinearity with their respective sub-dimensional variables, as indicated by a tolerance value of 0.000. The variable health literacy also exhibited significant collinearity, with a VIF of 11.381. Since the simultaneous inclusion of these aggregate variables and their subdimensions would have rendered the model unstable and statistically un-fittable, they were excluded in accordance with established statistical principles. Consequently, only the sub-dimensional variables were retained in the final model. This approach ensured the robustness of the model while enabling evaluation of the impact of specific components within each theoretical construct on decision-making behavior. After refitting, all remaining variables had VIF values below 4, indicating no substantial multicollinearity. The following variables were retained and subsequently entered into the multinomial logistic regression analysis: age, educational level, monthly per capita household income, functional health literacy, critical health literacy, objective support, subjective support, utilization of social support, need for information, need for deliberation, need for decisional control, ophthalmologist facilitation of patient involvement, and decision self-efficacy. The assigned values of the included independent variables are shown in Table 3.

Table 3 Assignment of Independent Variables

The likelihood ratio test indicated that the final model provided a significantly better fit than a null model (χ² = 272.002, df = 32, P < 0.001). This conclusion was bolstered by the pseudo R² values (Cox & Snell R² = 0.555; Nagelkerke R² = 0.634), which collectively suggest that the model captures a substantial proportion of the outcome’s variability and signals a good overall fit. The results identified age, monthly per capita household income, critical health literacy, ophthalmologist facilitation of patient involvement, objective support, and need for deliberation as significant independent predictors of patient decision-making roles. Compared to passive roles, patients adopting active roles tended to be younger, have higher income, report lower ophthalmologist facilitation, and express higher need for deliberation. Conversely, compared to passive roles, collaborative roles were associated with higher income, greater critical health literacy, stronger ophthalmologist facilitation, and elevated need for deliberation. Furthermore, when collaborative roles were compared to active roles, patients adopting the former were significantly older, reported higher objective support, and experienced greater ophthalmologist facilitation. The detailed results are shown in Table 4.

Table 4 Multinomial Logistic Regression Analysis of Factors Influencing the Actual Involvement in Decision-Making Roles Among Patients with Diabetic Retinopathy (n=336)

Discussion

To our knowledge, this is the first study to examine actual treatment decision-making involvement among Chinese patients with DR and explore its influencing factors using the COM-B framework. Overall, the passive role was the most common pattern observed. Furthermore, our analysis identified several key factors significantly influencing patient involvement: age, monthly per capita household income, critical health literacy, ophthalmologist facilitation of patient involvement, objective support, and need for deliberation.

Current Status of Patient Involvement in Treatment Decision-Making for Diabetic Retinopathy

The results revealed that 48.2% of patients reported passive roles, 30.7% reported collaborative roles, and 21.1% reported active roles. The proportion of passive role was relatively higher in patients with DR than in those with inflammatory bowel disease (32.12%),³⁷ gynecologic cancer (29.9%),³⁸ or atrial fibrillation (40.3%).²⁶ These differences may be attributed to distinct study populations and research settings. Specifically, DR has one of the lowest levels of public awareness compared with other ophthalmic diseases, which is likely due to the high professional barriers inherent in its diagnosis and treatment.³⁹ This low awareness is evidenced by a Chinese study where only 1.2% of diabetic patients could correctly identify symptoms of DR.⁴⁰ Furthermore, the combination of diverse treatment options, prognostic uncertainty, and the older demographics of the patient cohort may collectively impair patients’ understanding of the disease and therapeutic alternatives, thus predisposing them to passive decision-making.

Capability and Patient Involvement

Previous studies have pointed out that health literacy, encompassing various dimensions, is necessary for patient involvement in decision-making.^41,42 Successful patient involvement is predicated on patients possessing practical communication skills, the ability to acquire, comprehend, and communicate relevant information about disease and treatment from healthcare professionals, and critical evaluation skills.⁴¹ However, few studies have explored the distinct impacts of these different health literacy dimensions on patients’ actual involvement in decision-making. Our findings indicate that patients with higher critical health literacy are more likely to adopt collaborative decision-making roles. This result is consistent with a study conducted in the Netherlands, which found that critical health literacy was more important for patient participation than its functional and communicative counterparts.⁴³ In contrast, a French study reported a positive correlation for functional and communicative health literacy with patient involvement but no association for critical health literacy.⁴⁴ This discrepancy may be attributable to the relatively homogeneous scores for functional and communicative health literacy in our cohort, which exhibited limited variability. Our research suggests that the ability to simply acquire information and communicate with healthcare professionals is insufficient for meaningful participation in medical decision-making. Therefore, future interventions should focus on enhancing the critical health literacy of patients with DR, empowering them to analyze, evaluate, and question health information to make informed decisions that align with their personal values and preferences.

Opportunity and Patient Involvement

Our study revealed that increased ophthalmologist facilitation is significantly associated with patients adopting a collaborative role over either a passive or an active one. This suggests that when ophthalmologists proactively provide information, encourage questions, and respect patient concerns, they effectively bridge the inherent information and power asymmetry. Such empowerment fosters a climate of equitable dialogue, promoting patient engagement to collaborate rather than simply shifting the decision-making burden onto them. This finding aligns with a qualitative meta-summary which highlighted that clear information delivery, active listening, and trust-building are critical facilitators for patient engagement.⁴⁵ Furthermore, our results showed that greater ophthalmologist facilitation decreased the likelihood of an active role. A potential explanation is that highly facilitative ophthalmologists build a strong foundation of trust, engendering a sense of security and understanding in patients. Consequently, the perceived need for patients to assume a solely autonomous, active role diminishes. This perspective is supported by Kraetschmer’s research, which established that an active role is associated with low trust, a passive role with blind trust, and a collaborative role with high but not excessive trust.⁴⁶ Conversely, when patients perceive a lack of support or transparency, their trust may diminish, compelling them to adopt an active, patient-dominated role as a compensatory strategy to regain a sense of control.⁴⁷ Physician support, therefore, bridges the information and power gap, enabling true shared decision-making rather than pushing patients toward extremes.⁴⁸ This is further corroborated by a cross-sectional study which showed that physicians’ facilitative communication predicted greater patient participation.⁴⁹ Consequently, future interventions should focus on implementing training programs for ophthalmologists to enhance communication skills, integrate appropriate decision aids into practice, and foster patient empowerment and collaboration.

Visual impairment caused by DR leads to significant consequences, including disrupted family functioning, increased social isolation and dependence, and economic constraints, and inadequate social support is common in patients with DR.⁵⁰ Previous studies have established that social support promotes greater patient participation in decision-making by providing financial, emotional, and informational resources that reduce psychological stress and decision-making conflicts.^51–53 However, few studies have specifically evaluated the distinct impacts of different dimensions of social support on patient involvement in decision-making. Our study found that higher objective support was significantly associated with the adoption of a collaborative role in treatment decision-making. Objective support, in this context, refers to the tangible, visible, and practical assistance that patients receive from their social network, such as family, relatives and friends. Our study suggests that objective support is a more robust predictor of a patient’s decision-making role than are subjective support and the utilization of social support. Objective support provides the concrete resources necessary for shared information exchange, preference clarification, and joint deliberation.⁵⁴ This association is particularly salient in cultural contexts with strong family involvement and collectivist values, where health decisions are often regarded as a shared family responsibility.⁵⁵ In such settings, adequate objective support helps mitigate unilateral physician dominance while also alleviating the burden of solitary patient decision-making, thereby promoting a collaborative approach. Therefore, healthcare professionals should systematically assess the objective support levels of patients with diabetic retinopathy. Interventions should simultaneously focus on encouraging patients to strengthen their ties with social networks, such as family, relatives, and friends, and guide family members to become engaged partners in the patient’s treatment journey, thereby bolstering the decision-making support available to them.

Motivation and Patient Involvement

Need for decision-making reflects an individual’s intrinsic motivation to control the process and outcome of medical decisions.²⁸ Patients with a strong need for decision-making typically desire in-depth disease information, weighing pros and cons, participating in discussions, and having more personal control over treatment decisions. Charles et al categorized the treatment decision-making process into three components: information exchange, deliberation, and decision-making control.⁵⁶ The three dimensions of the PEPMDS correspond to the treatment decision-making process.⁵⁷ The univariate analysis results in our study indicate that all three dimensions are related to the patient’s decision-making role. In the multinomial logistic regression model, only the need for deliberation remained statistically significant. Our research suggests that the need for deliberation is a better predictor of patients taking a collaborative or active role in decision-making than the need for information and decision control. Logically, patients necessarily need access to sufficient medical information if they are to deliberate, and crucially, the process of deliberation often serves as a means for patients to strive for or achieve decision control. Therefore, when the need for deliberation is included in the model, it may have already captured most of the variation explained by the need for information and decision control. This finding suggests that patients, even if well-informed and possessing control, may still choose passive decision-making if they do not have the will to deliberate. Rather than just passively providing information or asking the patient about their willingness to take control of the decision, it is more important to identify whether patients are willing to think deeply, weigh the pros and cons, and discuss with healthcare professionals.

Other Factors Associated with Patient Involvement

Our research indicated that as patients age, those with diabetic retinopathy were more likely to adopt a passive or collaborative role in decision-making. This finding aligns with Salm’s study,⁵⁸ while it conflicts with the null results reported by Xie.¹⁵ Several contextual factors might explain this discrepancy. In contrast to Xie’s research, which involved undergraduate students and community-dwelling older adults in the United States and focused on general health decision-making scenarios, our work specifically examined patients with sight-threatening diabetic retinopathy. These patients face complex, urgent decisions, such as choosing intravitreal injections or laser surgery that directly affect their vision. For older adults, who may experience age-related cognitive decline or feel overwhelmed by technical medical information, the perceived complexity and high stakes of these decisions likely diminish their capacity to adopt proactive decision-making.⁵⁹ Furthermore, in the cultural context of our study, younger patients, compared with older patients influenced by traditional paternalistic norms in the doctor-patient relationship, tend to place greater emphasis on asserting personal autonomy and maintaining control over medical decisions.⁶⁰ Therefore, the effect of age may not be a simple main effect but is likely moderated by the disease context, the nature of the treatment decisions, and the cultural environment. We found no statistically significant association between gender and decision-making roles, a finding that stands in contrast to studies which concluded that females were more participatory or males were more involved.^16,17 This divergence suggests that the purported effects of gender may not be a stable, universal phenomenon but are likely contingent upon specific contextual moderators. Overall, there was no consistent evidence to support associations between decision-making roles and gender. Our findings presented that patients with a monthly per capita household income of ≤3000 yuan and 3001–5000 yuan were more inclined to take a passive role in actual decision-making for diabetic retinopathy compared to those with higher incomes. Similar to our results, Wang et al reported that patients with a monthly per capita household income of 5000–7999 yuan exhibited a greater tendency for collaborative decision-making than low-income patients.³⁷ This socioeconomic disparity in decision-making involvement is particularly consequential, given that diabetic retinopathy requires lifelong management involving significant ongoing costs. Treatments such as anti-VEGF therapy, while effective, are relatively expensive. In China, access to these therapies is further constrained by medical insurance policies requiring strict clinical indications and imposing limits on the cumulative reimbursable doses. Consequently, patients with lower household incomes often face substantial financial toxicity, which forces them to weigh treatment costs against potential efficacy and experience heightened psychological distress. This economic pressure frequently leads them to adopt a passive stance, accepting the more economical options recommended by physicians.⁶¹ In contrast, patients with higher incomes are less burdened by cost considerations. Their relative financial security, coupled with potentially greater access to comprehensive health information and support services, reduces the perceived burden of decision-making and fosters greater participation in choosing their treatment options. These observed disparities underscore the critical need for reforms in the national medical insurance payment system. Such reforms should aim to alleviate the financial barriers that currently prevent equitable access to preferred treatments and hinder the full participation of socioeconomically disadvantaged patients in treatment decision-making.

Strengths and Limitations

The study has two primary strengths. First, its theory-driven approach, underpinned by the COM-B model, allowed for a systematic and integrated analysis of the determinants of patient involvement, offering clear targets for future interventions. Second, its comprehensive assessment of key variables within the specific, high-stakes context of diabetic retinopathy enhanced the clinical relevance and applicability of the findings. This study has several limitations. First, the generalizability of our findings is limited by the sample, which was not only relatively small in size but also drawn from a single tertiary hospital in Shanghai. This specific context may not be representative of broader populations, particularly those in community clinics or rural settings where patient demographics and healthcare resources differ considerably. Second, the cross-sectional design cannot establish causal relationships between variables. Longitudinal studies are necessary to untangle these complex temporal dynamics and verify the directionality of these associations. Third, the reliance on self-reported data may introduce social desirability and recall biases, potentially affecting measurement validity. Future studies could incorporate objective measures, such as audio recordings of clinical encounters, to improve accuracy. Finally, our analytical strategy of using sub-dimensions limited our ability to quantify the overall impact of the integrated COM-B constructs. Given these limitations, the findings should be interpreted as exploratory and require validation in larger, more diverse cohorts. Future large-scale studies will also be better equipped to employ advanced statistical modeling techniques to further elucidate these complex relationships.

Conclusions

This study, the first to apply the COM-B model to decision-making involvement among DR patients in China, revealed a predominance of passive involvement among patients. Our findings suggested several potential determinants consistent with the COM-B framework, including capability factors such as critical health literacy, opportunity factors including ophthalmologist facilitation of patient involvement and objective support, and motivation factors like the need for deliberation, in addition to demographic variables such as age and income. These findings underscore the necessity for multifaceted interventions, including tailored patient education programs, clinician communication training, and accessible decision aids, to promote shared decision-making. However, given the constraints of the cross-sectional design and limited sample size, these conclusions should be considered exploratory. Future research should focus on large-scale longitudinal studies to track changes in patient decision-making roles over time, as well as intervention trials assessing the effectiveness of COM-B-based strategies in promoting shared decision-making.

Abbreviations

DR, diabetic retinopathy; COM-B, capability, opportunity, motivation, and behavior; CPS, control preference scale; AAHLS, all aspects of health literacy scale; SSRS, social support rating scale; FPIS, facilitation of patient involvement scale; DSES, decision self-efficacy scale; PEPMDS, patient expectation for participation in medical decision-making scale.

Data Sharing Statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Shanghai General Hospital (Approval No. 2024-098). Written informed consent was obtained from all participants.

Acknowledgments

The authors wish to thank all the patients and staff who participated in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The research did not get any dedicated financial funding from public, commercial, or not-for-profit funding organizations.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010;376(9735):124–136. doi:10.1016/s0140-6736(09)62124-3

2. Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology. 2021;128(11):1580–1591. doi:10.1016/j.ophtha.2021.04.027

3. Flaxman SR, Bourne RRA, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(12):e1221–e1234. doi:10.1016/s2214-109x(17)30393-5

4. Raj A, Singla A, Sidana S. Preventive and therapeutic strategies via health care delivery system to minimize sight-threatening diabetic retinopathy: a narrative review. Curr Diab Rep. 2025;25(1):36. doi:10.1007/s11892-025-01591-5

5. Shughoury A, Bhatwadekar A, Jusufbegovic D, Hajrasouliha A, Ciulla TA. The evolving therapeutic landscape of diabetic retinopathy. Expert Opin Biol Ther. 2023;23(10):969–985. doi:10.1080/14712598.2023.2247987

6. Salvetat ML, Pellegrini F, Spadea L, et al. The treatment of diabetic retinal edema with intravitreal steroids: how and when. J Clin Med. 2024;13(5). doi:10.3390/jcm13051327

7. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4(4):Cd001431. doi:10.1002/14651858.CD001431.pub5

8. Becker C, Gross S, Gamp M, et al. Patients’ preference for participation in medical decision-making: secondary analysis of the BEDSIDE-OUTSIDE trial. J Gen Intern Med. 2023;38(5):1180–1189. doi:10.1007/s11606-022-07775-z

9. Ruhnke GW, Tak HJ, Meltzer DO. Association of preferences for participation in decision-making with care satisfaction among hospitalized patients. JAMA Network Open. 2020;3(10):e2018766. doi:10.1001/jamanetworkopen.2020.18766

10. Li B. The power paradox of patient-centred care in Chinese community health: towards a conceptualisation. Soc Sci Med. 2025;371:117883. doi:10.1016/j.socscimed.2025.117883

11. Tang C, Wang A, Yan J. Exploring motivations and resistances for implementing shared decision-making in clinical practice: a systematic review based on a structure-process-outcome model. Health Expect. 2022;25(4):1254–1268. doi:10.1111/hex.13541

12. Truglio-Londrigan M, Slyer JT, Singleton JK, Worral P. A qualitative systematic review of internal and external influences on shared decision-making in all health care settings. JBI Libr Syst Rev. 2012;10(58):4633–4646. doi:10.11124/jbisrir-2012-432

13. Singh JA, Sloan JA, Atherton PJ, et al. Preferred roles in treatment decision making among patients with cancer: a pooled analysis of studies using the Control Preferences Scale. Am J Manag Care. 2010;16(9):688–696.

14. Fischer M, Visser A, Voerman B, Garssen B, van Andel G, Bensing J. Treatment decision making in prostate cancer: patients’ participation in complex decisions. Patient Educ Couns. 2006;63(3):308–313. doi:10.1016/j.pec.2006.07.009

15. Xie B, Wang M, Feldman R, Zhou L. Exploring older and younger adults’ preferences for health information and participation in decision making using the Health Information Wants Questionnaire (HIWQ). Health Expect. 2014;17(6):795–808. doi:10.1111/j.1369-7625.2012.00804.x

16. Lechner S, Herzog W, Boehlen F, et al. Control preferences in treatment decisions among older adults – results of a large population-based study. J Psychosom Res. 2016;86:28–33. doi:10.1016/j.jpsychores.2016.05.004

17. Torrente-Jimenez RS, Feijoo-Cid M, Rivero-Santana AJ, et al. Gender differences in the decision-making process for undergoing total knee replacement. Patient Educ Couns. 2022;105(12):3459–3465. doi:10.1016/j.pec.2022.08.014

18. Tang H, Dong S, Wang S, et al. Perceived participation in decision-making on primary surgery and associated factors among early breast cancer patients: a cross-sectional study. Cancer Nurs. 2023;46(2):111–119. doi:10.1097/ncc.0000000000001071

19. Jiang Y, Guo J, Sun P, et al. Perceptions and experiences of older patients and healthcare professionals regarding shared decision-making in pulmonary rehabilitation: a qualitative study. Clin Rehabil. 2021;35(11):1627–1639. doi:10.1177/02692155211010279

20. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42. doi:10.1186/1748-5908-6-42

21. Shi Y, Xie XY, Lao AD, Shao L, Wang ZA, Zhang JE. Prevalence of physical inactivity and its determinants among older adults living in nursing homes: a cross-sectional study based on COM-B model. J Clin Nurs. 2025;34(1):204–217. doi:10.1111/jocn.17325

22. Zhang M, Guo L, Namassevayam G, et al. Factors associated with health behaviours among stroke survivors: a mixed-methods study using COM-B model. J Clin Nurs. 2024;33(6):2138–2152. doi:10.1111/jocn.17103

23. Shpendi S, Norman P, Gibson-Miller J, Webster R. Cervical screening attendance in young women and people with a cervix: an application of the COM-B model. Br J Health Psychol. 2025;30(3):e70016. doi:10.1111/bjhp.70016

24. Chinn D, McCarthy C. All Aspects of Health Literacy Scale (AAHLS): developing a tool to measure functional, communicative and critical health literacy in primary healthcare settings. Patient Educ Couns. 2013;90(2):247–253. doi:10.1016/j.pec.2012.10.019

25. Lu L, Liu J, Yuan YC. Health information seeking behaviors and source preferences between Chinese and U.S. populations. J Health Commun. 2020;25(6):490–500. doi:10.1080/10810730.2020.1806414

26. Dong F, Wu Y, Wang Q, Huang Y, Wu Q. Factors influencing patient engagement in decision-making for catheter ablation of atrial fibrillation: a cross-sectional survey. Eur J Cardiovasc Nurs. 2025;24(1):150–157. doi:10.1093/eurjcn/zvae141

27. Wang Y, Qiu Y, Ren L, Jiang H, Chen M, Dong C. Social support, family resilience and psychological resilience among maintenance hemodialysis patients: a longitudinal study. BMC Psychiatry. 2024;24(1):76. doi:10.1186/s12888-024-05526-4

28. Wu H, Fan Y, Cheng Y, Zhang JE. Predictors of shared decision-making in patients with recurrent and metastatic nasopharyngeal carcinoma: an observational structural equation modeling approach. Eur J Oncol Nurs. 2025;76:102869. doi:10.1016/j.ejon.2025.102869

29. Rodríguez Del Águila M, González-Ramírez A. Sample size calculation. Allergol Immunopathol. 2014;42(5):485–492. doi:10.1016/j.aller.2013.03.008

30. Nolan MT, Hughes M, Narendra DP, et al. When patients lack capacity: the roles that patients with terminal diagnoses would choose for their physicians and loved ones in making medical decisions. J Pain Symptom Manage. 2005;30(4):342–353. doi:10.1016/j.jpainsymman.2005.04.010

31. Xu X. The patients satisfaction with participation in medical decision-making scale: development, reliability, and validity. 2010.

32. Dong F, Wu Y, Wang Q, Huang Y, Wu Q. Factors influencing patient engagement in decision making for catheter ablation of atrial fibrillation: a cross-sectional survey. Eur J Cardiovasc Nurs. 2024;2024:zvae141.

33. Xiao S. Theoretical basis and research application of social support rating scale. J Clini Psych. 1994;4(2):98.

34. Martin LR, DiMatteo MR, Lepper HS. Facilitation of patient involvement in care: development and validation of a scale. Behav Med. 2001;27(3):111–120. doi:10.1080/08964280109595777

35. Bunn H, O’Connor A. Validation of client decision-making instruments in the context of psychiatry. Can J Nurs Res. 1996;28(3):13–27.

36. Xu X, Mao J, Wang J, Zhao H. Developing strategy and item selection of the patients’ expectation for participation in medical decision making scale. China Mod Med. 2012;19:162–164.

37. Wang Y, Zhang S, Fang W, et al. Factors influencing decision-making preferences among patients with inflammatory bowel disease: a cross-sectional study in China. Patient Prefer Adherence. 2025;19:1047–1057. doi:10.2147/ppa.S517510

38. Abe M, Hashimoto H, Soejima A, et al. Shared decision-making in patients with gynecological cancer and healthcare professionals: a cross-sectional observational study in Japan. J Gynecol Oncol. 2025;36(3):e47. doi:10.3802/jgo.2025.36.e47

39. Scott AW, Bressler NM, Ffolkes S, Wittenborn JS, Jorkasky J. Public attitudes about eye and vision health. JAMA Ophthalmol. 2016;134(10):1111–1118. doi:10.1001/jamaophthalmol.2016.2627

40. Su C, Wang Z, Dong X, Ma X. Experiences of seeking diabetic eye care among patients with diabetes in China: a community-based convergent mixed methods study. Public Health. 2024;234:24–32. doi:10.1016/j.puhe.2024.05.021

41. Muscat DM, Shepherd HL, Nutbeam D, Trevena L, McCaffery KJ. Health literacy and shared decision-making: exploring the relationship to enable meaningful patient engagement in healthcare. J Gen Intern Med. 2021;36(2):521–524. doi:10.1007/s11606-020-05912-0

42. Bravo P, Edwards A, Barr PJ, Scholl I, Elwyn G, McAllister M. Conceptualising patient empowerment: a mixed methods study. BMC Health Serv Res. 2015;15:252. doi:10.1186/s12913-015-0907-z

43. Brabers AE, Rademakers JJ, Groenewegen PP, van Dijk L, de Jong JD. What role does health literacy play in patients’ involvement in medical decision-making? PLoS One. 2017;12(3):e0173316. doi:10.1371/journal.pone.0173316

44. Ousseine YM, Durand MA, Bouhnik AD, Smith A, Mancini J. Multiple health literacy dimensions are associated with physicians’ efforts to achieve shared decision-making. Patient Educ Couns. 2019;102(11):1949–1956. doi:10.1016/j.pec.2019.05.015

45. Mertens L, Kasmi T, Bekkering GE, et al. Shared challenges and opportunities: uncovering common ground in patient participation across different healthcare settings and patient groups. A qualitative meta-summary on patient-reported barriers and facilitators to participation in shared decision-making. Patient Educ Couns. 2025;130:108475. doi:10.1016/j.pec.2024.108475

46. Kraetschmer N, Sharpe N, Urowitz S, Deber RB. How does trust affect patient preferences for participation in decision-making? Health Expect. 2004;7(4):317–326. doi:10.1111/j.1369-7625.2004.00296.x

47. Tefera GM, Ngondwe P, Varol S. Trust matters: a qualitative study on healthcare access and utilization among African immigrants in the United States. J Community Health. 2025. doi:10.1007/s10900-025-01481-7

48. Scherer KA, Büdenbender B, Blum AK, et al. Power asymmetry and embarrassment in shared decision-making: predicting participation preference and decisional conflict. BMC Med Inform Decis Mak. 2025;25(1):120. doi:10.1186/s12911-025-02938-4

49. Street RL, Liu L, Farber NJ, et al. Keystrokes, mouse clicks, and gazing at the computer: how physician interaction with the EHR affects patient participation. J Gen Intern Med. 2018;33(4):423–428. doi:10.1007/s11606-017-4228-2

50. Fenwick E, Rees G, Pesudovs K, et al. Social and emotional impact of diabetic retinopathy: a review. Clin Exp Ophthalmol. 2012;40(1):27–38. doi:10.1111/j.1442-9071.2011.02599.x

51. Xiang JM, Gao LL. Decisional conflict, anxiety, and social support among Chinese pregnant women making further prenatal testing decisions. J Reprod Infant Psychol. 2025;43(1):34–46. doi:10.1080/02646838.2023.2232380

52. Wu M, Wang W, He H, Bao L, Lv P. Mediating effects of health literacy, self-efficacy, and social support on the relationship between disease knowledge and patient participation behavior among chronic Ill patients: a cross-sectional study based on the capability-opportunity-motivation and behavior (COM-B) model. Patient Prefer Adherence. 2025;19:1337–1350. doi:10.2147/ppa.S513375

53. Nguyen TQ, Le TD, Fan SY, Ke LS, Pham TVH, Kao CY. Exploring relational autonomy of vietnamese patients’ experiences in decision-making regarding hematopoietic stem cell transplantation: a qualitative interview study. Psychooncology. 2025;34(6):e70185. doi:10.1002/pon.70185

54. Wang H, Xiu M, Yang F, et al. Perceptions of shared decision-making participation during cardiac rehabilitation in coronary heart disease patients after coronary artery bypass surgery grafting: a qualitative study. BMC Cardiovasc Disord. 2025;25(1):529. doi:10.1186/s12872-025-04996-y

55. Lin ML, Huang CT, Chen CH. Reasons for family involvement in elective surgical decision-making in Taiwan: a qualitative study. J Clin Nurs. 2017;26(13–14):1969–1977. doi:10.1111/jocn.13600

56. Charles C, Whelan T, Gafni A. What do we mean by partnership in making decisions about treatment? BMJ. 1999;319(7212):780–782. doi:10.1136/bmj.319.7212.780

57. Yan S, Wang D, Huang Q, et al. Examining cancer patient preferences during three stages of decision making and family involvement: a multicenter survey study in China. BMC Med Inform Decis Mak. 2025;25(1):9. doi:10.1186/s12911-024-02846-z

58. Salm H, Schuler MK, Hentschel L, et al. Preferences on treatment decision making in sarcoma patients: prevalence and associated factors – results from the PROSa study. Oncol Res Treat. 2025;48(4):174–185. doi:10.1159/000543456

59. Drewniak D, Brandi G, Buehler PK, et al. Key factors in decision making for ECLS: a binational factorial survey. Med Decis Making. 2022;42(3):313–325. doi:10.1177/0272989×211040815

60. Sio TT, Chang K, Jayakrishnan R, et al. Patient age is related to decision-making, treatment selection, and perceived quality of life in breast cancer survivors. World J Surg Oncol. 2014;12:230. doi:10.1186/1477-7819-12-230

61. Ambigapathy R, Chia YC, Ng CJ. Patient involvement in decision-making: a cross-sectional study in a Malaysian primary care clinic. BMJ Open. 2016;6(1):e010063. doi:10.1136/bmjopen-2015-010063