Barriers and facilitators to shared decision-making for patients with cancer and health care providers based on the COM-B model: a systematic review

Abstract

Background

With advancements in cancer treatment approaches, patients face increasingly complex decisions regarding their care and treatment. Although Shared Decision-Making (SDM) can help patients make more informed and optimal choices, its development remains limited, and it has not been widely integrated into clinical practice. Identifying the barriers and facilitators to SDM from the perspective of patients and health care providers (HCPs) is essential. The Capability, Opportunity, and Motivation Model of Behaviour (COM-B) provides a framework for understanding these factors.

Objective

This review aimed to explore the barriers and facilitators of SDM between patients and HCPs on the basis of the COM-B and to identify key common and dual-effect factors.

Methods

Seven databases were searched for qualitative, quantitative, and mixed-methods studies. Data on the study design and key findings were extracted and analyzed guided by the COM-B model. The findings were reported in accordance with the PRISMA guidelines. Study quality was appraised via the Mixed Methods Appraisal Tool.

Results

A total of 6,811 papers were identified, 32 of which met the inclusion criteria. From these studies, 64 key barriers and facilitators influencing the implementation of SDM from the perspective of patients and HCPs were extracted. These factors were systematically categorized according to the subcomponents of the COM-B model: physical capability (e.g, poor health status), psychological capability (e.g, inaccurate understanding of the disease), reflective motivation (e.g, conflicting goals), automatic motivation (e.g, fear of cancer), physical opportunity (e.g, supplemental resources), and social opportunity (e.g, good family support).

Conclusion

Guided by the COM-B model, this study identified factors associated with SDM among patients with cancer and HCPs. Further analysis revealed that some factors are shared by both groups; interventions targeting these common factors may produce simultaneous effects on patients and HCPs, offering more implementation value. Other factors exhibit dual characteristics, acting as both facilitators and barriers depending on the context. Future efforts should focus on exploring mechanisms to address such barriers into facilitators in specific clinical settings, thereby promoting the widespread implementation of SDM.

Registration number

Our protocol was registered in PROSPERO (ID: CRD42024568101).

Supplementary information

The online version contains supplementary material available at 10.1186/s12911-025-03194-2.

Introduction

According to the latest estimates from the International Agency for Research on Cancer (IARC), nearly 20 million new cancer cases and 9.7 million cancer-related deaths occurred globally in 2022, making cancer a significant public health challenge that poses a serious threat to human health [1]. With the advancement of emerging medical approaches such as targeted therapy and immunotherapy, the survival prospects of patients with cancer have significantly improved [2]. However, increasingly complex treatment and care options have also intensified patients’ difficulties during decision-making [3]. Patients may feel a loss of personal agency and identity, and may lack a sense of control over their lives [4]. In addition, the acute and variable nature of cancer often requires patients to make multiple critical decisions within a short time, which can further lead to decision fatigue, conflict, and regret [5]. These experiences not only exacerbate anxiety, depression, and a decline in overall quality of life but also may result in irrational decision-making, leading to treatment delays or increased medical risks.

Shared Decision-Making (SDM) is an evidence-based approach that promotes collaboration between patients and healthcare providers (HCPs) in clinical decision-making. By exchanging information about the evidence (options, risks, and benefits) and the patient’s preferences and values, HCPs and patients can deliberate to determine the best treatment plan [6]. Studies have shown that SDM not only helps reduce decision conflict, regret, and fatigue [7] but also fosters greater trust between patients and HCPs [8], enhances patient satisfaction, and strengthens HCPs’ confidence and self-efficacy in clinical decisions [5]. Accordingly, SDM provides support for patients and HCPs in complex clinical decisions and contributes to improving the quality of both the decision-making process and its outcomes.

Despite the widely recognized benefits of SDM, its integration into routine clinical practice remains limited and faces numerous challenges. For example, while many patients with early-stage non-small cell lung cancer value involvement in treatment decisions, 40% still report decision conflict and limited understanding [9]. Similarly, another study reported that among 290 respondents, only 11% reported high engagement in SDM for nursing care, and only 26.9% reported high participation in treatment-related SDM [10]. Furthermore, although 25.3% of patients desired to engage in SDM, their actual participation rate was only 14.3% [11]. Awareness among HCPs remained low, with only 23.5% of physicians, 47.6% of nurses, and 19.5% of pharmacists reporting familiarity with the concept. These findings highlight a significant gap between patients’ expectations and the practical implementation of SDM in clinical settings.

Systematically identifying the barriers and facilitators to SDM adoption is crucial in this context. While previous studies have explored these factors, they often have limitations. On the one hand, some studies have not employed a structured theoretical framework, potentially leading to a lack of comprehensiveness and coherence in factor identification [10, 12–14]. On the other hand, some reviews focused solely on either qualitative or quantitative studies, limiting the comprehensiveness of evidence regarding factors influencing SDM [15, 16]. Moreover, most existing reviews were published before 2020, making it challenging to capture the latest developments in the SDM field [13, 17–19]. In addition, prior research has focused primarily on facilitators and barriers from the perspective of either patients or HCPs alone, with limited attention given to shared or overlapping factors between the two groups [19–24].

The Capability, Opportunity, and Motivation Model of Behaviour (COM-B) is a widely used theoretical framework in health behavior research that identifies the key determinants of behavior. It comprises three interrelated components: capability, opportunity, and motivation, each further divided into subcomponents-physical or psychological capability, social or physical opportunity, and automatic or reflective motivation [25]. In recent years, the COM-B model has been validated across various health behavior studies and applied to identify facilitators and barriers [26, 27], offering a structured basis for intervention design. Applying the COM-B model to analyze the facilitators and barriers to SDM can support a systematic and comprehensive understanding of the factors influencing SDM implementation in practice.

This study aimed to synthesize qualitative, quantitative, and mixed-methods research using the COM-B model to identify factors shared between patients and HCPs and key factors with dual roles. The identified factors were categorized according to Capability, Opportunity, and Motivation, and analyzed through a process of independent extraction, team discussion, consensus building, and coding aggregation to highlight common factors as well as key factors with dual roles. This study offers a comprehensive perspective and a theoretical basis for developing targeted, evidence-based strategies to promote wider adoption of SDM in clinical practice.

Methods

Design

We conducted a systematic review guided by the Cochrane Handbook for Systematic Reviews [28] and followed the PRISMA reporting guidelines [29] (see Check list).

Eligibility criteria

We used the PICOS framework to guide our eligibility criteria [30] (see Table 1, end of the document). Eligible studies were required to explore facilitators and/or barriers to SDM between patients with cancer and HCPs. A barrier was defined as any factor that obstructs or prevents SDM, whereas a facilitator was defined as a factor that supports or promotes SDM.

Table 1.

Study eligibility criteria

	Inclusion Criteria	Exclusion Criteria
Participants	HCPs and/or patients diagnosed with cancer, including cancer survivors if applicable.	Patients at high risk for cancer without a confirmed diagnosis. HCPs not directly involved in cancer care.
Intervention	SDM in cancer diagnosis, treatment, and survivorship care.	-
Comparison	All types of comparison groups, including studies with no comparison group.	-
Outcomes	Factors facilitating or hindering SDM in clinical and healthcare settings.	Studies focusing on the impact or effectiveness of SDM interventions on pati-ent outcomes.
Study methods	Empirical study designs, including qualitative, quantitative, and mixed-methods approaches.	Systematic reviews, narrative reviews, and unpublished studies.

* HCPs: Healthcare providers; SDM: Shared Decision-Making

Search strategy

We searched seven databases (PubMed, Web of Science, CINAHL, Embase, the Cochrane Library, PsycINFO, Scopus) from their inception to December 2024 (see Additional file 1). Four sets of search terms were designed to capture key aspects of the study. For example, the population search terms included “Health Personnel”, “Healthcare Workers”, “Cancer patients”, and “Oncology patients”; the intervention search terms included “Shared Decision Making” and “SDM”; the outcome-related search terms included “barriers”, “facilitators”, “challenges”, and “determinants”. Detailed search strategies for each database are provided in Additional file 1.

Study selection

We uploaded the retrieved literature to NoteExpress (Version 3.9.0.9588) for automatic deduplication on the basis of title, year, and author. Two researchers (Lisi Duan and Ting Wang) then independently screened the titles and abstracts according to the inclusion and exclusion criteria. Discrepancies were resolved by a third researcher (Chulei Tang). Full-text screening followed the same process, with reasons for exclusion documented in the PRISMA flow diagram. The research team reached a consensus on study selection, and the final included studies were subjected to data extraction and analysis.

Data extraction

We independently extracted the data via a standardized data extraction form. The extracted data included citation details (e.g., author, year, and location), study characteristics (e.g., design, methodological approach), participant types (e.g., patients, HCPs), and key objectives. Data extraction was independently conducted by two researchers (Lisi Duan and Ting Wang).

Quality assessment

Two researchers (Lisi Duan and Ting Wang) assessed the quality of the studies via the Mixed Methods Appraisal Tool (MMAT) [31]. This tool employs a standardized checklist approach to assess five core dimensions of study quality across qualitative, quantitative, and mixed-method designs, including methodological appropriateness, data collection adequacy, analytical rigor, and integration effectiveness in mixed-methods studies [32]. The MMAT has demonstrated reliability ranging from fair to perfect [33] and is particularly well suited for evaluating complex, context-dependent, and process-oriented interventions such as SDM. Any discrepancies between researchers were resolved through discussion and consensus. Since the objective of this review is to synthesize existing literature rather than screen or exclude studies, certain papers will not be excluded on the basis of the results of the quality assessment.

Data synthesis and analysis

Owing to the heterogeneity among the included studies in terms of design, participants, methods, and measurement approaches, the pooling of quantitative data was not appropriate. We imported the extracted text into NVivo (Version 12.0) and performed a thematic analysis to synthesize barriers and facilitators to SDM [34].

Specifically, the thematic analysis involved the following steps: first, prominent themes were identified from the extracted data; next, these themes were classified according to the components of the COM-B model (Capability, Opportunity, and Motivation), and each theme was further assigned to the corresponding sub-components to systematically determine the facilitators and barriers, in line with the theoretical framework of the COM-B model.

All themes were independently extracted and coded by two researchers (Lisi Duan and Ting Wang). During the categorization process, we followed expert guidelines [25] and resolved discrepancies through team discussions to reach a consensus. The guidelines provided definitions of the COM-B components and their sub-components, offering detailed guidance and reference for theme classification. The specific content extracted is provided in Additional file 2 (see Additional file 2). The representative original citations and their corresponding examples of facilitators and barriers used during this process are provided in Additional file 3 (see Additional file 3).

Results

Identified studies and characteristics

Our search initially identified 6811 citations (see Fig. 1). After removing duplicates and screening titles and abstracts, we conducted a full-text review of 83 articles, ultimately including 32 studies in our analysis. These studies, published between 2007 and 2024, originated from 13 countries: the USA (n = 8), China (n = 5), the Netherlands (n = 4), Australia (n = 3), Norway (n = 2), Malaysia (n = 2), Spain (n = 2), and one each from Korea, Japan, Iran, Jordan, Germany, and France. Barriers and facilitators were reported from the perspective of HCPs (n = 20) and the perspective of patients (n = 22). The specific information of those studies is presented in Table 2 (see Table 2, end of the document).

Fig. 1.

Flowchart illustrating the process of inclusion and exclusion of papers in the study

Table 2.

Characteristics of the included studies

Author, year	Countries	Patients	HCPs	Methodological approach	Design	Study objectives related to this systematic review
Yu, 2024 [35]	Korea	5 patients with cancer	4 nurses and 4 doctors	Qualitative	Semi‑structured interviews	Explore the decision-making process among medical professionals and patients.
Abe, 2024 [11]	Japan	154 patients	153 physicians, 166 nurses, and 154 pharmacists	Quantitative	Cross-sectional study	Identify differences and similarities between patient and HCP ideas for implementing optimal SDM.
van Hienen, 2024 [36]	Netherlands	1799 patients	-	Quantitative	Cross-sectional study	Understand patients’ preferences and experiences regarding SDM and to identify targets for SDM implementation.
Gu, 2024 [37]	China	24 patients with low-risk papillary thyroid cancer	-	Qualitative	Semi‑structured interviews	Explore the experience and influencing factors of patients’ in SDM.
Otrebski Nilsson, 2023 [38]	Norway	5063 patients with prostate cancer	-	Quantitative	Retrospective cohort	Explore factors associated with the wish to be involved in and experiences of SDM.
Orstad, 2023 [39]	Norway	12 patients with incurable lung cancer	12 nurses and 18 doctors	Qualitative	Semi-structured focus groups	Explore needs and challenges among patients and HCPs when making decisions about advanced lung cancer treatment.
Wang, 2022 [10]	China	290 patients with lung cancer	-	Quantitative	Cross-sectional study	Explore factors that influence SDM on treatment and care of lung patients with cancer.
Karuturi, 2022 [12]	USA	26 patients with breast cancer	-	Mixed methods	Semi-structured interviews and cross-sectional study	Determine the perspectives and experiences of older patients with breast cancer in the treatment decision-making process.
Lee, 2022 [40]	Malaysia	12 patients with breast cancer	16 HCPs and 5 policymakers	Qualitative	Focus group discussions and in-depth interviews	Explore barriers to practising SDM faced by HCPs and patients.
Cao, 2022 [41]	China	34 patients with prostate cancer	16 medical and nursing staffs	Qualitative	Semi-structured interviews	Investigate the factors that impact the participation in surgical decision-making among prostate patients with cancer.
Steenbergen, 2022 [42]	Netherlands	-	5 nurses, 11 residents, 4 oncologists, and 2 healthcare managers	Qualitative	Focus groups and semi-structured interviews	Explore healthcare providers’ perspectives on SDM for oncology inpatients and identify barriers and facilitators.
Lawhon, 2021 [43]	USA	33 patients with breast cancer	-	Qualitative	Semi-structured interviews	Explores factors influencing SDM in older patients with early-stage breast cancer.
Lowenstein, 2019 [44]	USA	30 patients with lung cancer	12 physicians	Qualitative	Parallel semi-structured interview	Explore attitudes and priorities among physicians and patients that inform SDM about lung cancer screening in real-world settings.
Padilla Garrido, 2019 [23]	Spain	-	351 physicians	Quantitative	Cross-sectional Study	Identify the perceptions of the physicians on main barriers and facilitators to SDM use.
Chang, 2019 [24]	Taiwan, China	120 patients with cancer	-	Quantitative	Cross-sectional Study	Explore the SDM situation and influencing factors in patients with cancer.
Gruß, 2019 [45]	USA	11 patients with breast cancer	6 Clinicians	Qualitative	An ethnographic and interview study	Understand patient challenges to SDM goals and values.
Chung, 2019 [46]	Taiwan, China	-	400 physicians, pharmacists, nurses, and other HCPs	Quantitative	Cross-sectional Study	Explore health care professionals’ perceptions of SDM.
Aminaie, 2019 [47]	Iran	15 patients with cancer	-	Qualitative	Semi‑structured interviews	Explore perceptions about barriers to decision‑making in Iranian patients.
Obeidat, 2018 [48]	Jordan	-	86 Jordanian medical and radiation oncologists and surgeons	Quantitative	A cross-sectional exploratory survey	To determine Jordanian physicians’ perceived barriers and facilitators to patient participation in treatment decision‑making.
Brom, 2017 [49]	USA	14 patients with cancer	-	Qualitative	Face-to-face in-depth interviews and observations	Gain insight in patients’ and physicians’ experiences of and views on SDM.
Lawn, 2017 [50]	Australia	11 cancer survivors	2 family caregivers, and 8 clinicians and researchers	Qualitative	Group discussion	Enablers and barriers to advancing shared care.
Mokhles, 2017 [51]	Netherlands	-	110 Lung cancer clinicians	Quantitative	Cross-sectional Study	Query barriers to and drivers of SDM in clinical practice.
Ortega-Moren, 2017 [52]	Spain	118 patients with cancer	-	Quantitative	Cross-sectional Study	Facilitating and barriers of SDM implementation.
Colley, 2017 [53]	USA	941 patients with cancer	-	Quantitative	Cross-sectional Study	Explore factors associated with oncology patients’ involvement in SDM during chemotherapy.
Hasak, 2017 [54]	USA	20 patients with breast cancer	20 surgeons and nurses	Qualitative	Semi-structured interviews	Explore facilitating and barriers of SDM implementation.
Engelhardt, 2016 [55]	Netherlands	105 patients with breast cancer	-	Qualitative	Observational study	Explore potential barriers to SDM.
Frerichs, 2016 [56]	Germany	-	43 HCPs	Qualitative	Focus groups and interviews	HCPs’ experiences with SDM in current practice.
Lee, 2016 [57]	Malaysia	-	11 urologists, 5 urology trainees, 3 oncologists and 1 policy maker	Qualitative	In-depth individual interviews and focus group discussions	Explore the challenges faced by HCPs in Malaysia in supporting patients with early prostate cancer in SDM.
McCarte, 2016 [58]	USA	-	30 nurses	Qualitative	Semi-structured interviews	Identify the barriers and promoters for participation in cancer treatment decision in the era of SDM process.
Beaussant, 2015 [59]	France	29 patients with cancer	17 physicians	Mixed methods	Prospective observational and in-depth interviews.	Explore subjective determinants of the decision-making process from the physicians’ and the patients’ perspectives.
Shepherd, 2011 [60]	Australia	-	22 doctors	Qualitative	Telephone interviews	Investigate factors that motivate cancer doctors to involve their patients in treatment decisions.
Shepherd, 2007 [61]	Australia	-	604 doctors	Quantitative	Cross-sectional Study	Explore facilitating and barriers of SDM implementation.

* HCPs: Healthcare providers; SDM: Shared decision-Making

Study appraisal

The included studies employed qualitative (n = 18; 56.3%), quantitative (n = 12; 37.5%), or mixed methods (n = 2; 6.3%). All qualitative studies used an appropriate approach and adequate data collection methods. Biases included missing reports on whether findings were adequately derived from the data (n = 2/18; 11.1% missed), whether interpretations were substantiated by data (n = 2/18; 11.1% missed), and whether coherence existed between data sources, collection, analysis, and interpretation (n = 4/18; 22.2% missed/unsure). In quantitative studies, all employed a relevant sampling strategy and appropriate statistical analysis. Biases included missing reports on sample representativeness (n = 3/12; 25.0% missed/unsure), measurement appropriateness (n = 1/12; 8.3% unsure), and risk of nonresponse bias (n = 6/12; 50.0% missed/unsure). All mixed-method studies address the rationale for using a mixed-methods design, integrating study components, interpreting combined qualitative and quantitative findings, handling inconsistencies, and adhering to methodological quality criteria (see Table 3, end of the document).

Table 3.

Results of the MMAT appraisal

Type of study	Qualitative studies					Quantitative descriptive studies					Mixed methods
MMAT items	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O
Qualitative studies
Yu, 2024 [35]	○	○	○	○	○
Gu, 2024 [37]	○	○	○	○	○
Orstad, 2023 [39]	○	○	○	○	○
Lee, 2022 [40]	○	○	○	○	◎
Cao, 2022 [41]	○	○	○	○	○
Steenbergen, 2022 [42]	○	○	○	○	○
Lawhon, 2021 [43]	○	○	○	●	○
Lowenstein, 2019 [44]	○	○	●	○	○
Gruß, 2019 [45]	○	○	○	○	○
Aminaie, 2019 [47]	○	○	○	○	○
Brom, 2017 [49]	○	○	○	○	◎
Lawn, 2017 [50]	○	○	○	○	○
Hasak, 2017 [54]	○	○	●	○	◎
Engelhardt, 2016 [55]	○	○	○	●	○
Frerichs, 2016 [56]	○	○	○	○	◎
Lee, 2016 [57]	○	○	○	○	○
McCarte, 2016 [58]	○	○	○	○	○
Shepherd, 2011 [60]	○	○	○	○	○
Quantitative studies
Abe, 2024 [11]						○	○	○	○	○
van Hienen, 2024 [36]						○	○	○	○	○
Otrebski Nilsson, 2023 [38]						○	●	○	○	○
Wang, 2022 [10]						○	●	○	○	○
Padilla Garrido, 2019 [23]						○	○	◎	●	○
Chang, 2019 [24]						○	○	○	◎	○
Chung, 2019 [46]						○	○	○	○	○
Obeidat, 2018 [48]						○	○	○	○	○
Mokhles, 2017 [51]						○	○	○	◎	○
Ortega-Moren, 2017 [52]						○	◎	○	◎	○
Colley, 2017 [53]						○	○	○	●	○
Shepherd, 2007 [60]						○	○	○	●	○
Mixed methods
Karuturi, 2022 [12]											○	○	○	○	○
Beaussant, 2015 [59]											○	○	○	○	○

MMAT: Mixed Methods Appraisal Tool

A. Is the qualitative approach appropriate to answer the research question? B. Are the qualitative data collection methods adequate to address the research question? C. Are the findings adequately derived from the data? D. Is the interpretation of results sufficiently substantiated by data? E. Is there coherence between qualitative data sources, collection, analysis and interpretation? F. Is the sampling strategy relevant to address the research question? G. Is the sample representative of the target population? H. Are the measurements appropriate? I. Is the risk of nonresponse bias low? J. Is the statistical analysis appropriate to answer the research question? K. Is there an adequate rationale for using a mixed methods design to address the research question? L. Are the different components of the study effectively integrated to answer the research question? M. Are the outputs of the integration of qualitative and quantitative components adequately interpreted? N. Are divergences and inconsistencies between quantitative and qualitative results adequately addressed? O. Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?

○ = yes; ● = no; ◎ = unsure

SDM barriers and facilitators

A total of 64 key barriers and facilitators influencing the implementation of SDM from the perspective of patients and HCPs were extracted. To present these factors more clearly, Table 4 (see end of document) maps all factors individually to the COM-B subcomponents. Specifically, 22 barriers and 16 facilitators were identified for patients, whereas 16 barriers and 10 facilitators were identified for HCPs. Guided by the COM-B model, these factors were classified into six domains: psychological capability, physical capability, reflective motivation, automatic motivation, physical opportunity, and social opportunity. For instance, under psychological capability, patients frequently reported limited health literacy [10, 39, 41, 47, 51, 53, 58] and inaccurate understanding of disease [10, 11, 23, 38, 43, 48, 53, 56] as barriers, whereas facilitators included high health literacy [10, 23, 24, 36, 41, 44, 45, 47, 56] and information processing abilities [39, 45, 51, 56]. In terms of opportunity, both patients and HCPs highlighted time constraints [23, 31, 40–42, 47, 50–52, 54], lack of resources [11, 39–42, 44, 50, 57], and limited access to information [10–12, 37–41, 47] as major barriers, while organizational support and the use of decision aids [24, 39–41, 43, 45, 46, 50–52, 57] were noted as facilitators. Within the motivation domain, barriers included conflicting goals [39, 40] and HCPs’ negative beliefs about SDM [23, 44, 52], whereas facilitators comprised patients’ trust in their providers [47, 48, 54, 57] and positive attitudes toward SDM [23, 45, 47, 51, 52, 58]. Each factor was accompanied by references indicating its source, and the number of citations reflected the frequency with which each factor appeared in the literature.

Table 4.

Classification and analysis of SDM barriers and facilitators via the COM-B model

COM-B	Patients		HCPs
	Barriers	Facilitators	Barriers	Facilitators
PsC	Inaccurate understanding of the disease [10, 11, 23, 38, 43, 48, 53, 56] Limited health literacy [10, 39, 41, 47, 51, 53, 58] Poor communication skills [10, 40, 42, 52, 57, 60]	High health literacy [10, 23, 24, 36, 41, 44, 45, 47, 56] Information processing abilities [39, 45, 51, 55]	Poor communication skills [23, 41, 42, 50, 57, 59] Lack of knowledge and experience [41, 42, 50, 51] Lack of confidence [50]	Good communication skills [23, 35, 42, 50, 60] Professional knowledge and experience [42–44, 51, 58]
PhC	Poor health status [10, 38, 40–43, 51, 53] Comorbidities [38, 39, 43]	-	-	-
RM	Conflicting goals [39, 40] Perceived differences in decision preferences [36, 44, 51, 59] Uncertainty about the outcome of a decision [37, 47, 59]	Trusting in HCPs [47, 48, 54, 57] Decision-making confidence [48] The positive attitude towards SDM [36, 37, 44, 50, 54, 56]	A miscalculation of patient preferences [40, 56, 60, 61] Uncertainty about the outcome of a decision [39, 40, 57, 59] Conflicting goals [39, 40]	Uncertainty about the outcome of a decision [60]
AM	Fear of cancer [10, 40, 42, 44, 47, 53, 60] Less open personality [10, 53]	Fear of cancer [44, 57] Extroverted personality [10]	Negative values and beliefs about SDM [23, 44, 52] Physicians’ priority focus on biomedical information [45]	The positive attitude towards SDM [23, 45, 47, 51, 52, 58]
PO	Lack of time [23, 31, 40–42, 47, 50–52, 54] Resource shortages [23, 42, 43, 47, 57, 60] Financial pressure [10, 11, 44, 57] Access to information is limited [10–12, 37–41, 47–49, 51, 52, 54, 56, 61] Lack of privacy [42, 50] Underdeveloped insurance system [10, 44, 58]	Supplemental resources [10, 24, 31, 40, 43, 44, 50, 52, 54, 57] The doctor’ s clinician experience [43] Access to information is limited [48, 61] Financial pressure [10, 53, 57]	Lack of time [40, 42, 44–46, 48, 51, 54, 56, 57, 59] Lack of workforce [40, 50, 57] Lack of continuity of care [40, 42, 46] Resource shortages [11, 39–42, 44, 50, 57] Lack of training [44, 51, 57, 59]	The utilization of decision aids [24, 39–41, 43, 45, 46, 50–52, 57] Organizing training on educating and counselling [11, 40, 43, 47, 51] Electronic health record optimization [42, 50]
SO	Judgemental attitudes from HCPs [40, 54] Religious or cultural conflicts [40, 46, 47] Family interference [38, 40, 41, 48] Power imbalance in doctor-patient relationships [40, 47, 48, 50] Social comparison pressure [47, 55] Bias against different patients from clinicians [54]	Good family support [10, 12, 38, 43, 48] Absence of family support [10] Limited educational attainment [10, 24, 38, 41, 47, 53] Peer success stories [12] Disease culture [47, 60]	Language and cultural barriers [40] Organizational and policy constraints [50, 58] Patient-related disorders [39, 48, 57, 60]	Cross-disciplinary collaboration [11, 50, 56–58, 60] Good patient-physician relationship [42, 49, 50] Cultural background of patients [11, 35, 48, 54, 60]

* HCPs: Healthcare providers; SDM: Shared decision-Making; COM-B (Sub-compon): The Capability, Opportunity, and Motivation Model of Behaviour; PsC: Psychological capability; PhC: Physical capability; RM: Reflective motivation; AM: Automatic motivation; PO: Physical opportunity; SO: Social opportunity

Patient-level factors

Barriers

Psychological capability

Patients often face difficulties engaging in SDM due to an inaccurate understanding of the disease [10, 11, 23, 38, 43, 48, 53, 56], limited health literacy [10, 39, 41, 47, 51, 53, 58], and poor communication skills [10, 40, 42, 52, 57, 60]. Some patients lack an understanding of the relationships among disease progression, treatment side effects, and survival outcomes [38], making it challenging to process complex medical information [10]. Those with low health literacy tend to have reduced information needs [41] and insufficient ability to weigh different treatment options [51]. Additionally, inadequate communication skills hinder effective interactions with HCPs [40, 52].

Physical capability

Poor health status [10, 38, 40–43, 51, 53] and comorbidities [38, 39, 43] are significant barriers. Patients often experience symptoms such as pain, nausea, and dyspnea [42], along with limitations in physical strength and cognitive functioning, which impair their ability to engage in decision-making. Additionally, multiple comorbidities further compromise patients’ autonomy. In some cases, adverse treatment effects force patients to discontinue therapy [39], diminishing their willingness and capacity to participate in decision-making.

Reflective motivation

Patients often face challenges in actively participating in SDM because of conflicting goals [39, 40], perceived differences in decision preferences [36, 44, 51, 59], and uncertainty about outcomes [37, 47, 59]. For example, the need to balance therapeutic benefits against side effects or to weigh quality of life against treatment efficacy can reduce patients’ willingness to engage in decision-making [39, 40]. Moreover, misalignment between patients’ and healthcare providers’ preferences may cause patients to feel that their values and priorities are not fully acknowledged [51, 59]. Uncertainty about the treatment outcomes further contributes to patients’ hesitation and avoidance of medical decisions [59].

Automatic motivation

Fear of cancer [10, 40, 42, 44, 47, 53, 60] and a less open personality [10, 53] are key factors limiting patient participation in SDM. Following diagnosis, many patients experience persistent fear and anxiety [10, 60], and some hold fatalistic beliefs equating cancer with death [44, 53], which hinders their engagement in the decision-making process. In addition, patients who are introverted, have difficulty expressing themselves or are reluctant to initiate communication often adopt passive or avoidant decision-making attitudes [10, 53], reducing their proactiveness in SDM.

Physical opportunity

Patients are often constrained by external factors such as time pressure [23, 31, 40–42, 47, 50–52, 54], limited resources [23, 42, 43, 47, 57, 60], financial pressure [10, 11, 44, 57], and insufficient informational support [10–12, 37–41, 47–49, 51, 52, 54, 56, 61]. Restricted consultation time [23, 39, 47, 61], transportation difficulties [43], and inadequate medical infrastructure [47, 57] hinder patients’ ability to understand and participate fully in treatment decisions. Heavy financial burdens and inadequate insurance coverage [10, 44, 58] further limit access to high-quality healthcare. In addition, underdeveloped decision support systems, asymmetrical information exchange, and mismatched content [41, 51, 52] impair patients’ understanding and evaluation of treatment options. The absence of a private and reassuring communication environment [42] also suppresses patients’ willingness and ability to express their preferences.

Social opportunity

Participation in SDM is influenced by healthcare provider-patient interactions, family involvement, and sociocultural factors. Some HCPs may hold judgmental attitudes [40, 54] or biases toward certain patients [54], limiting the expression of patients’ preferences. Power imbalances between patients and providers undermine patients’ intention in treatment [40, 47, 48]. In specific cultural contexts, treatment decisions are often led by spouses or family members [40, 47]. However, family involvement may lead to conflicting goals and sometimes deprive patients of their decision-making autonomy [40, 41]. Social comparison or peer pressure can also discourage active patient participation [55].

Facilitators

Psychological capability

High health literacy [10, 23, 24, 36, 41, 44, 45, 47, 56] and information processing abilities [39, 45, 51, 55] serve as facilitators of SDM. Patients with high health literacy often express a strong desire to acquire disease-related knowledge [10], clearly articulate their preferences and needs [41], and engage more actively in decision-making processes [24]. Moreover, well-developed abilities to comprehend and evaluate information enable patients to grasp essential medical content [39, 45, 56], perform rapid assessments, and make informed decisions more effectively.

Reflective motivation

Trust in HCPs [47, 48, 54, 57], decision-making confidence [48], and a positive attitude toward SDM [36, 37, 44, 50, 54, 56] are important enablers. Trust in providers enhances patients’ sense of security and willingness to cooperate [48, 54]. Confidence in making decisions encourages patients to express their preferences and participate in treatment planning [48]. Patients who hold favorable views of SDM are more likely to embrace their role in decision-making and actively engage in provider-patient communication [50].

Automatic motivation

Cancer-related fear [44, 57] and extroverted personality [10] are facilitators of SDM participation. Some patients become more proactive in decision-making due to fear of cancer, aiming for early intervention to reduce perceived risk [45, 57]. Extroverted individuals are typically more expressive and communicative, demonstrating greater willingness to participate in treatment discussions [10].

Physical opportunity

Supplemental resources [10, 24, 31, 40, 43, 44, 50, 52, 54, 57], physicians’ clinical experience [43], informational limitations [48, 61], and financial stress [10, 53, 57] drive patient involvement. Decision coaches, informational booklets, and decision aids offer clear guidance and support, whereas experienced physicians improve communication quality and promote patient engagement [10, 40, 43, 44]. Furthermore, limited access to information [61] and financial pressures [57] may prompt patients to actively participate in obtaining more effective information and pursuing lower-cost treatment options.

Social opportunity

Family support [10, 12, 38, 43, 48], educational attainment [10, 24, 38, 41, 47, 53], peer success stories [12], and disease-related cultural factors [47, 60] contribute positively to patient involvement in SDM. Patients with strong family support—particularly those with more children—tend to be more willing to participate in decision-making [10, 44]. Conversely, patients lacking family support, such as those who are divorced or widowed, may also take a more active role in decisions [10]. Although patients with lower educational levels may initially exhibit a passive stance, they can be encouraged to participate when guided by healthcare professionals [53]. In addition, peer experiences with successful treatment and supportive disease culture can enhance decision-making confidence and motivation to engage [43].

Provider-level factors

Barriers

Psychological capability

HCPs are often hindered by poor communication skills [23, 41, 42, 50, 57, 59], a lack of knowledge and experience [41, 42, 50, 51], and a lack of confidence [50]. Inadequate communication abilities among HCPs limit patients’ access to sufficient and comprehensible information [23, 41]. A lack of professional knowledge and clinical experience further restricts the ability to provide patients with detailed explanations of their conditions and treatment options, compromising the quality of decision guidance [44]. Additionally, low confidence in their capabilities discourages HCPs from actively promoting SDM [24].

Reflective motivation

Significant barriers include miscalculating patient preferences [40, 56, 60, 61], uncertainty regarding treatment outcomes [39, 40, 57, 59], and goal conflicts [39, 40] between patients and providers. Some physicians underestimate patients’ willingness to participate in decision-making [40] and thus refrain from initiating discussions or sharing decision-related information. When treatment decisions involve uncertain risk-benefit profiles, providers may delay or avoid SDM because of concerns about unpredictable outcomes [59]. Moreover, conflicts between clinical goals, such as balancing treatment continuation with side effect management, often lead physicians to adopt a more unilateral approach to decision-making [39].

Automatic motivation

Negative attitudes toward SDM [23, 44, 52] and an overemphasis on biomedical information [45] pose challenges. Some providers hold traditional, paternalistic views of their role in clinical care and prefer to dominate treatment decisions [52], limiting patient engagement. In clinical communication, biomedical facts are often emphasized while patients’ values and preferences are overlooked, weakening the foundation for meaningful SDM [45].

Physical opportunity

Time constraints [40, 42, 44–46, 48, 51, 54, 56, 57, 59], workforce shortages [40, 50, 57], a lack of continuity in care [40, 42, 46], limited resources [11, 39–42, 44, 50, 57], and inadequate training [44, 51, 57, 59] are barriers. HCPs often manage multiple responsibilities without dedicated coordinators or case managers, making it difficult to allocate sufficient time and attention to SDM activities [40, 52]. Disruptions in the continuity of care hinder personalized responses to patients’ evolving concerns [40]. Furthermore, limited access to SDM-related training diminishes providers’ confidence and capacity to engage in SDM practices effectively [43].

Social opportunity

Language and cultural differences [40], organizational and policy constraints [50, 58], and patient-related factors [39, 48, 57, 60] impede the implementation of SDM. Language and cultural barriers often constrain effective communication, whereas patients’ cognitive or expressive limitations further complicate the decision-making process [40]. Institutionally, the absence of standardized guidelines and support mechanisms for nursing involvement in SDM restricts nurses’ contributions, especially in oncology settings [58]. Additionally, privacy regulations and institutional policy inconsistencies hinder information exchange and interprofessional collaboration [50].

Facilitators

Psychological capability

Good communication skills, professional knowledge and experience [42–44, 51, 58] facilitate SDM among HCPs. HCPs with strong communication abilities can better assess patients’ willingness to participate in decisions and guide the process more effectively [41]. In addition, patients’ trust in their providers’ clinical expertise fosters a sense of comfort and safety, enhancing collaborative decision-making [43].

Reflective motivation

Uncertainty regarding treatment outcomes [60] can prompt greater emphasis on SDM. When faced with situations involving unclear risk-benefit ratios or insufficient evidence, some physicians prefer to engage patients in exploring treatment options to distribute decision-making responsibility [60].

Automatic motivation

A positive attitude toward SDM and recognition of its value and benefits [23, 45, 47, 51, 52, 58] are important facilitators. When HCPs recognize that SDM can improve patient experiences and optimize clinical outcomes—and when they receive positive feedback from patients—they are more likely to develop intrinsic motivation to implement SDM practices [52].

Physical opportunity

The utilization of decision support tools [24, 39–41, 43, 45, 46, 50–52, 57], structured training programs [11, 40, 43, 47, 51], and optimized electronic health records [42, 50] contributes to the implementation of SDM. Decision aids help simplify the communication of complex medical information, thereby improving patients’ comprehension and engagement [40, 41, 43, 50]. Training programs in counselling and patient education strengthen HCPs’ communication and support skills [51]. Moreover, well-designed electronic health records enhance the accessibility and sharing of medical information, providing technical infrastructure to support SDM [42].

Social opportunity

Cross-disciplinary collaboration [11, 50, 56–58, 60], good provider-patient relationships [42, 49, 50], and patient cultural background [11, 35, 48, 54, 60] act as facilitators for SDM implementation. Multidisciplinary care models enhance communication among HCPs and increase expectations for patient participation in decision-making [50, 56]. A strong therapeutic alliance between HCPs and patients establishes a foundation of trust essential for SDM [43]. Patients’ cultural backgrounds also offer important cues that allow providers to assess information needs and participation preferences better, enabling more tailored and practical decision guidance [43].

Discussion

Application of the COM-B model in the SDM

Guided by the COM-B model, this study systematically identified and categorized the facilitators and barriers to SDM between patients with cancer and HCPs. The results showed that the COM-B model effectively encompasses the key factors influencing both parties’ participation in SDM. Compared with previous studies based on the Theory of Planned Behavior (TPB) [62], this study further integrated external factors such as environmental conditions, resources, and social support, thereby enhancing the model’s explanatory power regarding institutional barriers and cultural contexts at the societal level. Although some studies have applied the Theoretical Domains Framework (TDF) to offer more detailed 14-domain classifications [63], their complexity and conceptual overlap have limited its practical application.

Moreover, this study synthesized and identified key factors across multiple levels within the COM-B model, expanding the scope of previous research. At the opportunity level, factors such as privacy protection [42, 50], institutional and organizational support, social and family influences, and decision aids [24, 39–41, 43] were extracted. At the capability level, health literacy [10, 36, 41], information processing ability [39, 45, 51, 56], and HCPs’ communication skills [23, 41, 42, 50] were identified. At the motivation level, patients’ decisional uncertainty [37, 47, 59] and decision preferences [36, 44, 51, 59] were further recognized, providing new insights into the facilitators and barriers influencing SDM. In summary, these factors derived within the COM-B framework not only extend the explanatory power of existing theories but also provide innovative theoretical support for the development of multidimensional and actionable SDM intervention strategies.

Although the application of the COM-B model in SDM offered many insights, there were also some challenges during data analysis. While most themes could be categorized within the COM-B framework, some did not fit neatly into a single subcomponent. This is because opportunity and capability can influence motivation, and behaviour, in turn, can modify capability, motivation, and opportunity [64]. For example, patients’ uncertainty about decision outcomes (reflective motivation) [37, 47, 59] can be influenced by their capability (e.g., health literacy [10, 39, 41, 47, 51, 53, 58], information-processing abilities [39, 45, 51, 56]) and opportunity (e.g., decision aids [24, 39–41, 43, 45, 46, 50–52, 57], family support [38, 40, 41, 48]), while engaging in SDM behaviors can further enhance their capability and opportunity, thereby strengthening motivation. Similarly, HCPs’ negative beliefs about SDM (reflective motivation) may be shaped by their capability (knowledge [41, 42, 50, 51], communication skills [23, 41, 42, 50, 57, 59]) and opportunity (training [44, 51, 57, 59], organizational support [50, 58]), and participation in SDM can improve both their attitudes and resources. These examples indicate that COM-B subcomponents are dynamically interactive rather than independent, making some themes difficult to classify strictly. At the same time, this also highlights the need to consider interactions among factors when designing intervention strategies.

Common facilitators and barriers to SDM among patients with cancer and HCPs

A thorough understanding of the most common facilitators and barriers encountered by patients with cancer and HCPs in the process of SDM is essential for identifying the key challenges in current clinical practice.

For patients with cancer, the most common barriers include limited access to information, lack of time, and inaccurate understanding of the disease. The most frequently reported facilitators are supplemental resources, high health literacy, and a positive attitude towards SDM. In clinical practice, it is crucial to allow patients adequate time to consider their decisions and to provide comprehensive and easily understandable decision-making information to help them better understand disease-related knowledge. For patients with lower educational levels, the use of decision aids can be an effective way to support their active involvement in decision-making. Previous studies have shown that decision aids can reduce patients’ decision regret and increase their participation in decision-making [65]. Furthermore, patients’ attitudes play a key role in their engagement in SDM; research has demonstrated that a positive attitude significantly promotes SDM behaviors among patients with cancer [66]. Therefore, efforts should be made to improve patients’ negative perceptions of SDM and enhance their willingness to participate.

For HCPs, the most common barriers include lack of time, resource shortages, and poor communication skills. Facilitators include the use of decision aids, cross-disciplinary collaboration, and the organization of training programs focused on patient education and counselling. In practice, healthcare institutions should allocate human resources appropriately and reduce the workload of professionals to ensure that they have sufficient time to engage in SDM with patients. Additionally, increasing investment in resources and strengthening training in communication skills and professional knowledge are vital for fostering effective multidisciplinary collaboration. Research has shown that training programs aimed at improving HCPs’ education and counselling skills can increase their confidence and capacity to carry out SDM [67]. Moreover, the development and application of decision aids have been shown to save time and improve decision-making efficiency [68]. This is consistent with findings from existing studies. Therefore, greater financial and technical support is needed to advance the development and integration of decision aids into clinical practice.

It is worth noting that the common barriers and facilitators for patients and HCPs in SDM are largely influenced by healthcare system factors, such as resources, organizational support, training, and policies. Thus, SDM interventions should address both individual and system-level factors to improve feasibility, effectiveness, and sustainability in clinical practice.

Shared barriers and facilitators to SDM among patients with cancer and HCPs

Building on previous research, this review further reveals that although patients and HCPs assume different roles in the SDM process, their behaviors are influenced by several shared factors. Identifying the shared factors of both parties contributes to a more comprehensive understanding of the interactive mechanisms of SDM. Additionally, identifying the shared factors also enables intervention strategies to benefit both patients and providers simultaneously, thereby enhancing the efficiency and effectiveness of interventions under limited resource conditions.

Among the barriers, “lack of time” emerged as the most prominent. Patients often hesitate to ask questions because of concerns about physicians’ busy schedules [40] or face urgent clinical decisions that limit opportunities for thorough communication [51, 59]. Moreover, HCPs are frequently constrained by heavy workloads and tight schedules, often leading to rushed patient communication [23, 40]. “A lack of knowledge and experience” also poses a mutual challenge. Patients may struggle to understand disease and treatment information due to limited health literacy and restricted access to information, whereas some HCPs lack adequate training and experience in SDM practices. Additional shared obstacles include limited resources, conflicting decision goals, uncertainty about outcomes, poor communication skills, and cultural differences. Notably, existing interventions have demonstrated that these barriers can be effectively addressed. For example, training for cancer physicians significantly improved their SDM capabilities, while their patients showed marked reductions in anxiety and depression [5]. Similarly, a multi-level SDM training program significantly enhanced physicians’ SDM behaviors during consultations [69]. These cases indicate that mapping barriers and facilitators to the COM‑B model and designing interventions accordingly not only provides theoretical guidance but also produces tangible impacts in clinical practice.

Among facilitators, the use of decision aids is the most common shared factor. These tools assist patients in understanding complex medical information [40, 52] and enable HCPs to communicate more effectively [10]. Moreover, a positive attitude toward SDM and prior experience with SDM were identified as mutual enablers for both patients and providers. Previous studies have emphasized that SDM involves reciprocal behaviors shaped through interaction. Rather than a unidirectional process, SDM is a dynamic exchange grounded in mutual understanding and collaboration [70]. Thus, identifying the shared determinants of SDM behavior in both patients and HCPs has practical significance, providing clear targets for intervention to enhance engagement on both sides.

Dual-role factors that both facilitate and hinder SDM among patients with cancer and HCPs

Some factors influencing SDM are not inherently facilitators or barriers but rather exhibit context dependency and duality. Depending on the context, their impact may vary across individuals, environments, or stages of the care process, functioning as enablers or obstacles. Identifying these context-dependent factors that have dual attributes can help us adjust the intervention strategies more specifically and improve their adaptability and practical effectiveness.

For instance, the influence of economic status is not unidirectional for either patients or HCPs. In countries with inadequate healthcare coverage, treatment costs may lead economically disadvantaged patients to forgo care or avoid SDM altogether [53, 58]. However, for some patients, financial pressure may motivate greater engagement with clinicians in discussing treatment costs and benefits in pursuit of more affordable options [10]. Similarly, family support can have a bidirectional effect. While familial involvement often facilitates patients’ access to and understanding of information [12, 38], the absence of support (e.g., due to widowhood or divorce) may, in some cases, prompt greater autonomy and a more proactive role in SDM [10]. Additionally, emotional responses such as fear of cancer represent a double-edged sword. On the one hand, fear may trigger avoidance or denial, reducing the willingness to participate in decision-making [10, 42, 52]. On the other hand, it can also drive patients to seek more active interventions, increasing their level of engagement [44]. Likewise, perceptions of uncertainty regarding treatment outcomes may elicit divergent behavioral responses. Some patients may withdraw from decisions out of anxiety [40], whereas others may become more proactive in seeking physician guidance [39]. Therefore, it is crucial to recognize and account for these influencing factors’ situational and dual natures when promoting SDM in clinical settings. Rather than rigidly classifying them as facilitators or barriers, intervention strategies should be designed with flexibility and contextual sensitivity. As emphasized in previous research, many barriers are modifiable. The multi-component SDM implementation program at the German Cancer Center transformed healthcare professionals’ lack of SDM knowledge and low patient engagement into facilitating factors, significantly improving patient-reported SDM uptake and HCPs’ SDM knowledge [71]. Similarly, The Canadian “Breamy” augmented reality mobile health application helped breast cancer patients better understand treatment options, converting knowledge gaps and decision-related anxiety into proactive decision-making behaviors, thereby enhancing patients’ decision confidence and satisfaction [72]. These examples demonstrate that, with targeted support and guidance at the patient, provider, and organizational levels, such barriers can be transformed into facilitators, thereby enhancing the widespread adoption and integration of SDM into routine clinical practice [17].

Implications and suggestions

The findings of this study highlight the importance of implementing SDM from both the patient and HCPs’ perspectives by systematically identifying and addressing the underlying components of capability, motivation, and opportunity. At the capability level, efforts should focus on enhancing patients’ health literacy and information comprehension while strengthening HCPs’ communication and decision-support skills, thereby establishing a solid cognitive and skill-based foundation for SDM. At the motivational level, interventions aim to increase patients’ awareness of the value of SDM, bolster their confidence and initiative in decision-making, and foster HCPs’ intrinsic motivation by promoting respect for patient preferences and cultivating empathy. At the opportunity level, optimizing the healthcare system environment is essential. This includes improving the allocation of time, human, and informational resources; promoting the use of decision aids; and encouraging interdisciplinary collaboration to create favorable external conditions for SDM. Future intervention designs must account for the most common factors and the context-dependent nature of influencing factors. Tailored and dynamic support strategies should be developed on the basis of individual characteristics and decision-making contexts to facilitate the broad and sustainable integration of SDM into clinical practice.

Strengths and limitations

Guided by the COM-B model, this review systematically and comprehensively analysed the facilitators and barriers to SDM among patients with cancer and HCPs. The notable strengths of this review lie in its identification of the most commonly reported facilitators and barriers, the overlapping factors shared by both patients and HCPs, and the dual-role factors that may function either as barriers or facilitators depending on context. These findings offer clearer and more precise guidance for the design of targeted interventions to promote SDM in practice. However, several limitations should be acknowledged. First, the included studies were primarily conducted in Western countries and China, with limited representation from other regions, potentially constraining the cultural generalizability of the findings. Second, owing to considerable heterogeneity in research methods and measurement tools, a meta-analysis could not be conducted, and thematic synthesis was adopted instead. Given the diversity of samples and data types, we employed a frequency-based approach to summarize relevant factors without incorporating effect sizes. Furthermore, qualitative analysis may be subject to researcher interpretation bias. Finally, when synthesizing data from multiple sources, there is a risk of inconsistency between participants’ original statements and our interpretations [73]. Future research should strengthen cross-cultural evidence, improve methodological consistency, and enhance transparency in analytical procedures.

Conclusion

Guided by the COM-B model, this review systematically identified facilitators and barriers to SDM among patients with cancer and HCPs. It further synthesized the most common factors, those shared by both patients and healthcare professionals, as well as factors with dual roles. The study revealed that certain factors may act as facilitators or barriers depending on the clinical context, highlighting the necessity of developing context-sensitive intervention strategies. These findings provide a theoretical basis for designing targeted behavioral interventions aimed at promoting the effective implementation of SDM in oncology clinical practice, thereby addressing the complex challenges faced by patients with cancer in treatment decision-making.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data extraction and analysis were performed by Lisi Duan, Ting Wang. The first draft of the manuscript was written by Lisi Duan Ting wang and Chulei Tang. The second draft of the manuscript was checked by Yinning Guo, Zhongmin Fu, Ting Xu, Ping Zhu, Liuliu Zhang and Shijuan Gao. The quality of the entire article was controlled by Qin Xu and Chulei Tang. All authors read and approved the final manuscript.

Funding

This work was supported by the Jiangsu Provincial Natural Science Foundation for Higher Education Institutions (22KJB320013); National Natural Science Foundation of China (NSFC) (72404140); National Natural Science Foundation of China (NSFC) (82073407); The 4th Priority Discipline Development Program of Jiangsu Higher Education Institutions (Jiangsu Education Department (2023) No.11).

Data availability

All data used in this study are available in the included primary studies.

Declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Nanjing Medical University (Date 2024.07.16/No 2022(811)). The researcher obtained informed consent from all participants involved in this study. The participants were assured that they could withdraw from the study at any time.

Consent for publication

Not applicable to the manuscript.

Competing interests

The authors declare no competing interests.