1. Introduction
Patient and public involvement and engagement (PPIE), consumer engagement, co-design, and co-production are often used interchangeably in healthcare research, reflecting the involvement of relevant individuals in planning, designing, and/or delivering research [1,2,3,4,5,6]. In this review, we will use PPIE to incorporate these different terms. PPIE represents a collaborative partnership between researchers and patients or the public. It refers to the involvement of patients and the public in key stages of the research process, including problem identification, study design, selection of outcome measures, execution of research, interpretation of results, evaluation of research outcomes, and dissemination of findings [7,8,9]. There is a well-established consensus that embedding PPIE in health-related research improves the relevance, quality, effectiveness, and impact of the research [3,6,9]. For healthcare interventions, PPIE has become increasingly recognized for its contributions to improving intervention resource utilization, relevance, acceptability, and efficacy [10,11,12], which has led to a substantial increase in related research publications. However, there remains a gap in a global and up-to-date review of the bibliometric profiles, themes, and historical development trends in healthcare intervention-related studies reporting PPIE. Understanding these bibliometric characteristics and trajectories is important for researchers. It helps them identify relevant focal points for future publications in healthcare intervention fields, seek collaborations across authors, institutions, and countries, and use PPIE in healthcare interventions for diverse populations. For practitioners, these research features can inform practical ideas and foster collaboration with appropriate scholars.
PPIE is increasingly being integrated into the development of healthcare services and the formulation of healthcare policies [13]. By aligning research with patient needs and public concerns, PPIE helps to direct the focus of healthcare services towards these critical issues [14]. Including patients and the public in the design of research methodologies improves both the practicality and the acceptability of the services proposed [6]. Insights from PPIE provide essential real-world perspectives that guide policy decisions and facilitate policy adaptations that meet practical implementation challenges and match patients’ and the public’s preferences [15]. The integration of diverse voices into research also increases the likelihood of gaining social support, thereby strengthening advocacy for policies derived from the research [13]. Furthermore, PPIE is considered a requirement for securing some research funding in the UK, with funding bodies like the National Institute for Health and Care Research (NIHR) mandating its inclusion in applications [4]. Applicants need to detail PPIE from a project’s design to its execution and impact evaluation on standard forms and specify budget allocations for this purpose [4]. Regarding healthcare interventions, PPIE helps shape the initial purpose, set priorities, and frame agendas during the planning stage of an intervention [2]. During a design stage, PPIE ensures that end-users’ preferences and needs are adequately considered and discussed for increasing the relevance, acceptability, and effectiveness of an intervention [10,16]. At the implementation stage, PPIE fosters a sense of ownership and empowerment among relevant stakeholders, driving higher levels of engagement and participation [16]. By encouraging patient and public participation in decision-making processes and providing customized education about interventions, healthcare researchers and professionals can align healthcare plans more closely with users’ preferences, thereby improving clinical outcomes and building long-term trust [17,18]. Ultimately, PPIE not only improves individual patient and public care but also influences broader practices, making them more responsive to the real-world complexities and needs of healthcare [17].
Earlier reviews on PPIE in healthcare primarily used systematic and narrative literature reviews [19,20,21]. These reviews contribute to the description and evaluation of specific questions related to the use of PPIE in healthcare [22,23]. However, there is a lack of a global overview on the research features and trends of studies reporting PPIE in healthcare interventions. A bibliometric review (BR) can complement previous review methods by offering a broader and more objective analysis, systematically evaluating bibliometric features including research areas, journals, authors, affiliations, keywords, themes, the literature, trends, and collaboration networks [24]. BR utilizes mathematical and statistical tools to visually and statistically analyze large-scale publications within a research field over the years, presenting foundational research components such as authors, institutions, journals, keywords, and references [24]. It also identifies the main research themes and developmental trends to provide a clear picture of the current status of the field and guide future studies [25,26].
2. Review Aim and Question
This study aimed to conduct a BR to elucidate the bibliometric characteristics and trends in healthcare intervention-related studies reporting PPIE from an international perspective to inform future research and practices. Applying a general BR approach to the existing literature, the specific review questions are as follows:
Review question 1 (RQ1): What are the yearly distributions of published articles, authors, journals, and influential authors/journals/references in healthcare intervention-related studies reporting PPIE?
Review question 2 (RQ2): What are the distributions of countries and their trends in healthcare intervention-related studies reporting PPIE?
Review question 3 (RQ3): What are the themes and their trends in healthcare intervention-related studies reporting PPIE?
We consider “intervention-related studies” to be those that focus on evaluating the effects and outcomes of specific strategies or programs designed to improve health, behavior, or social conditions [27]. The methodologies for healthcare interventions are diverse, including randomized controlled trials, clinical trials, therapeutic interventions, and other types [28,29]. Our focus is specifically on research that reports PPIE in healthcare interventions. It is not required that these studies employ PPIE as a primary intervention. This study followed the preliminary guidelines for reporting bibliometric reviews of biomedical literature (BIBLIO) [30] and the preferred reporting items for bibliometric analysis (PRIBA) in health and medicine-related studies [31] to structure each section of this review.
3. Materials and Methods
3.1. Database Selection
This study employed the Web of Science (WoS) as the sole database. In bibliometric research, the WoS database is highly regarded for its detailed and comprehensive record-keeping, particularly in medicine, health sciences, nursing, and healthcare [32]. It serves as a crucial bibliographic repository and is instrumental in research evaluations and bibliometric analysis [32]. While other databases are also valuable for review studies, the WoS distinguishes itself with its systematic approach, quality of publications, and advanced bibliometric capabilities including detailed indices and more accurate data extraction (e.g., author names and citation counts) [33,34].
3.2. Data Collection and Data Period
This study collected BR data from targeted sub-datasets within the Web of Science (WoS) Core Collection, including the Social Sciences Citation Index (SSCI), Science Citation Index Expanded (SCI-E), Emerging Sources Citation Index (ESCI), and Arts & Humanities Citation Index (A&HCI), known for containing high-quality and influential publications [33,35]. To create a comprehensive database for healthcare intervention-related studies reporting PPIE, the study did not set a start date and included all publications available up to 26 November 2024 (the date when the data were collected).
3.3. Search Strategy and Inclusion Criteria
To ensure the comprehensiveness and accuracy of data searching, the research team outlined a search strategy for systematic data collection based on prior PPIE studies and team discussions. We incorporated a range of terminologies linked to our conceptualization of PPIE, supplemented with synonyms from the Medical Subject Headings [36]. For instance, terms included not only “PPIE” but also “co-production”, “co-creation”, “co-design”, “consumer involvement”. “Patients” were defined broadly to include patients, carers, and users of health and social care services, along with their advocates; “Public” referred to all other potential service recipients and stakeholders [37]. Non-human and animal studies were excluded. Keywords relating to involvement and engagement were entered into the WoS “all field” search in case terms like “patient involvement” were not explicit in the title or abstract [37,38]. The search terms used to identify “intervention-related studies” included types of interventions referenced in prior research [27,28,29], such as “randomized controlled trial”, “clinical trial”, “factorial”, “placebo”, and “therapy”. This review is limited to English healthcare intervention-related studies that have reported PPIE and are published in the WoS. The decision to focus on the WoS was based on its rigorous indexing standards, comprehensive coverage of high-quality, peer-reviewed research, and advanced bibliometric tools that facilitate reliable and systematic analyses [32,33,34]. While it is acknowledged that other databases, such as Scopus or PubMed, could provide additional insights, the WoS was selected for its multidisciplinary scope, robust citation metrics, and proven reliability in bibliometric research. However, this selection may inherently exclude relevant studies indexed in other databases, which is recognized as a limitation of this review. Additionally, the research focus is not on studies exclusively about interventions on PPIE but rather on those where terms related to both interventions and PPIE are identified. The search strategy and inclusion criteria are shown in Figure 1.
3.4. Data Analysis
To address the review questions, we employed a bibliometric analysis of WoS and CiteSpace. CiteSpace 6.3.R1, an updated version of the CiteSpace series tool particularly useful for BR, was selected for this study. The bibliometric analysis of WoS is frequently employed to systematically examine foundational research characteristics of publications indexed in WoS, such as the number of yearly publications, authors, countries, institutions, and journals [33]. CiteSpace was specifically selected for its academic popularity and advanced capabilities in visualizing internal relationships, identifying emerging trends, and analyzing thematic patterns within diverse research areas [39]. Its user-friendly interface, combined with its professionalism and diverse bibliometric functions, makes it well suited for conducting in-depth bibliometric evaluations [33,40]. Both tools have been extensively utilized in bibliometric review studies across disciplines [33,39,40].
After removing duplicates of OMC articles using CiteSpace, we found that the earliest articles in our review emerged in 1991, totaling six from that year, and thus set the main parameters in CiteSpace as follows: time slicing (January 1991–November 2024), years per slice (1), term source (title, abstract, author, keywords, and keywords plus), node type (author, institution, country, keyword, source, and reference), selection criteria (top 10% of most-cited or occurred items from each slice), g-index (k = 25), pruning (pathfinder, pruning the merged network), and visualization (cluster viewstatic, showing merged network). The specific data analysis methods used to identify each bibliometric feature are shown in Table 1.
4. Results
This study conducted a BR of studies related to healthcare interventions reporting on PPIE, sourced from the WoS Core Collection, over recent decades. The review exclusively considered English-language articles focused on healthcare interventions that reported PPIE and were published in the WoS. The scope of this review extends beyond studies solely dedicated to interventions on PPIE to include those studies where both interventions and PPIE-related terms are identified. Our systematic data collection strategy was meticulously outlined, incorporating a broad spectrum of terminologies associated with our conceptualization of PPIE. This was further enriched with PPIE synonyms from the MeSH, methods employed in interventional studies, and various healthcare-related terms.
4.1. Yearly Published Articles, Authors, Influential Authors, and Journals
4.1.1. Yearly Distribution of Published Articles, Journals, and Authors
As shown in Figure 2, PPIE being referred to in healthcare intervention articles began appearing in 1991, with a gradual increase over the following two decades. A marked rise is evident from 2018 onwards. According to Table 2, the top journals publishing these articles include BMJ Open, BMC Health Services Research, Health Expectations, and other high-impact journals. Most of them are open access from medical fields. The most prolific authors by number of publications are Wells Kenneth, Joseph Tucker, Lovell Karina, Mullins Daniel, Montori Victor Manuel, and Chaboyer Wendy.
4.1.2. Distribution of Influential Authors/References/Journals
The top ten authors, based on citation counts, are listed in Table 3. These authors have made significant contributions to healthcare intervention-related studies reporting PPIE and are considered influential in this area. Among these, authors representing the World Health Organization received the highest citation count, followed by Vittoria Braun (Charité-Universitätsmedizin Berlin, Germany), Susan Michie (University College London, UK), Albert Bandura (Stanford University, USA), and Trisha Greenhalgh (University of Oxford, UK).
Similarly, the top articles based on citation counts were identified and subsequently verified by the authors (see Table 4). These articles were not original interventional studies employing PPIE to change subjects’ outcomes. Instead, they were identified based on the presence of terms related to both interventions and PPIE within the search system. These articles offer frameworks and guidelines that contribute to the understanding of the evolution and importance of PPIE in healthcare intervention-related studies [13,14,15,46]. They also demonstrate various formats for implementing PPIE in the development and evaluation of interventions [2,21,47], and discuss practical applications of PPIE and its impact in diverse settings [16,48,49].
Table 5 also lists the top ten journals and their citation counts. It was found that PLOS ONE had the highest citation count (2791) in the study field, followed by BMJ (2668), the Lancet (2656), and JAMA (2379).
4.1.3. Distribution of Author Collaboration
The author collaboration network is illustrated in Figure 3. The analysis identified 1273 nodes (authors) and 1853 collaboration links and reveals several prominent co-authorship clusters, indicating concentrated efforts among specific groups of researchers to advance knowledge in healthcare interventions reporting PPIE. Notable leaders within these clusters include Beaton Dorcas, who maintained 19 collaborative links, and Baba Ami, Smith Maureen, Hofstetter Catherine, Markham Sarah, Butcher Nancy, Elsman Ellen, Ricketts Juanna, Haywood Kirstie, and Farmer Julie, each with 18 collaborative links. These authors occupy central positions within the network, indicating their roles in driving collaborative research within this domain.
A closer examination of the network reveals an uneven distribution of collaboration, with significant clustering around key individuals and groups. It suggests that while certain authors are interconnected, other areas of the network exhibit weaker collaborative ties. Such disparities highlight opportunities for fostering broader interdisciplinary engagement and integrating less-connected researchers into more active networks. The reliance on central authors, while beneficial for facilitating collaboration and knowledge exchange, may also pose challenges, such as bottlenecks in research innovation or over-dependence on a few researchers to sustain international collaborations in the field. Clusters with high connectivity demonstrate the potential to accelerate the exchange of ideas, drive innovation, and contribute to a more cohesive body of knowledge. A more active and even collaborative network is needed for a more inclusive and diverse research community to foster greater equity in the generation and dissemination of knowledge.
4.2. Countries
4.2.1. Distribution of Countries and Country Collaboration
The distribution of countries is shown in Table 6. The majority of these studies originated from the USA (40.73%), England (22.50%), Australia (12.82%), and Canada (9.36%). The collaborative network among countries, shown in Figure 4, comprises 74 nodes and 2082 links. Lebanon leads with the highest number of collaborative links (n = 9), followed by Argentina, Chile, and South Africa with 6 each, and Canada, Singapore, Denmark, India, Belgium, and Poland with 4 each. Notably, Lebanon, South Africa, and India demonstrated betweenness centrality values exceeding 0.5, followed by Belgium, Argentina, Chile, Singapore, Nepal, and Slovenia. The network highlights a dynamic and influential collaboration landscape in global healthcare intervention research related to PPIE.
Regarding the distribution of institutions, the most productive institutions are predominantly based in the USA and the UK (see Table 7). The institutional collaboration network, consisting of 863 nodes and 969 links, revealed an uneven distribution (see Figure 5). Notable institutions include Johns Hopkins University (US) with 15 links, the University of Ottawa (Canada) and Harvard University (US) with 14 links each, and the University of Sydney (Australia) with 11 links.
4.2.2. Trends of Countries/Regions
Figure 6 identifies the top 20 countries/regions exhibiting the strongest citation bursts. The USA displayed the strongest citation burst (17.36), indicating that PPIE healthcare intervention research from the USA attracted the most substantial global attention and was cited the highest between 2008 and 2010. Other longer periods of PPIE healthcare intervention research development were observed in Scotland from 2004 to 2008 and Vietnam from 2020 to 2024. From 2021 to 2024, Asian countries such as Singapore, Malaysia, and Saudi Arabia experienced a surge in PPIE healthcare intervention research, indicating the growing prominence of Asia in the global research landscape in this field. The expanding contributions from Asia also demonstrate its increasing influence on recent global PPIE healthcare intervention research directions and highlight the potential need for cross-regional collaboration to translate regional outcomes into broader healthcare progress.
4.3. Themes
Identification of Themes
The Web of Science (WoS) database identified the top ten research areas of healthcare intervention-related studies reporting PPIE (see Table 8). These articles span diverse disciplines, demonstrating the interdisciplinary nature of healthcare intervention research reporting PPIE.
These research areas represent a broad and overarching set of disciplines or categories as recognized by the WoS classification system. Building upon this initial analysis, a subsequent theme investigation was conducted using the article title, keyword and abstract extraction, and clustering function in CiteSpace. This allowed for the identification of more focused, specific, and subject-matter-oriented research themes. The top ten distinct clusters were identified and are presented in Table 9, including “community participation”, “health equity”, “coronary heart disease”, “web-based patient empowerment”, “mental illness”, “obesity prevention”, “intervention mapping”, “healthy behavior”, “reporting guidance”, and “special populations network”. Cluster size reflects similar article counts, while homogeneity is measured by a Silhouette score, where a score close to 1 signifies high similarity within the cluster [33]. A mean score above or nearly equal to 0.5 is deemed acceptable [50]. Table 9 also details key terms and the mean publication year for each cluster, highlighting “community participation”, “obesity prevention”, and “special populations network” as the earliest studied themes. A representative article for each cluster is highlighted.
4.4. Trends of Themes
Figure 7 presents the top 25 keywords with the most substantial citation bursts as identified by CiteSpace, suggesting these terms represent the dominant themes in PPIE healthcare interventional studies during the specified years. The keyword “community participation” exhibited the highest burst strength at 45.83, reflecting its centrality and enduring influence in the field. The prominence of keywords like “community participation”, “patient participation”, and “consumer participation” demonstrates the foundational role of participatory approaches in shaping PPIE healthcare research and practices. It also reflects a growing recognition of the importance of engaging communities, patients, and consumers as active stakeholders in healthcare decision-making and intervention design. Their high burst strengths and extended periods of influence suggest that they have been well integrated into the research.
Other significant keywords, such as “randomized trial” (1999–2019), “prevention” (1995–2015), “intervention” (1995–2015), and “education” (1995–2014), demonstrated longer burst cycles which indicate their sustained relevance over extended periods. They may also point to a sustained emphasis on rigorous methodologies, preventive approaches, and educational strategies to improve healthcare outcomes. More recent keywords with shorter burst periods include “mobile phone” (2021–2024) and “digital health” (2022–2024), reflecting emerging trends and the evolving focus on technology-driven healthcare interventions that utilize PPIE. The shorter burst periods of these keywords may also signal the rapid pace of innovation and adoption in digital health, as well as the responsiveness of the field to technological advancements.
Overall, the thematic evolution of keywords highlights the dynamic and multidimensional nature of research within PPIE healthcare interventions. The coexistence of long-standing themes with emerging trends suggests that while the field continues to build on established foundations, it is also highly adaptive to new challenges and opportunities. The growing focus on “digital health” and “mobile phone” interventions may reflect efforts to address global health challenges, such as disparities in access to care, by leveraging scalable and cost-effective solutions. Meanwhile, the enduring relevance of participatory approaches reinforces the imperative to center research around the needs and perspectives of patients, consumers, and communities.
5. Discussion
5.1. Increasing Publications and Collaborative Challenges
To our knowledge, our BR is the first one to describe bibliometric characteristics and evolving trends in healthcare intervention-related studies reporting PPIE in recent decades. Our results indicate that globally implemented studies using PPIE have grown considerably over the past two decades, with a particularly notable increase in the last five years. Particularly in the past three years, there has been a significant increase in the Asian region. The growing emphasis on PPIE in healthcare intervention-related studies may be due to its increasing recognition by researchers and strong advocacy from research organizations such as the UK Medical Research Council and NIHR [2,4,13]. International guidelines such as those from the Declaration of Helsinki and the European Clinical Trials Regulation continually highlight the importance of PPIE in healthcare [58,59]. These efforts collectively promote a transparent, respectful, and inclusive research process, advocating for the integration of patient and public insights throughout healthcare interventions.
Our results suggest that most authors work independently or in pairs within the same organization. The most prolific, influential, and actively collaborating authors are distinct from each other. Most collaborating institutions are within the same country, highlighting a predominance of internal over cross-national collaboration and a lack of stable international partnerships. Cross-national collaborative links in PPIE healthcare intervention-related studies are sparse, with the most productive countries demonstrating less active collaboration with other nations in this field. Cross-national cooperation faces challenges such as language and cultural barriers, misinterpretations, and data security concerns [60,61]. Geographical limitations also pose obstacles, especially when involving multiple clinics or health organizations across extensive regions [62]. Socioeconomic and demographic variations further influence cross-national practices in healthcare interventions [63]. Additionally, patients from different geographical regions have varying needs and perspectives, complicating the development of universally applicable healthcare interventions. Countries or regions with less resources to implement PPIE in healthcare interventions may also present challenges for cross-national cooperation [7,9]. Limited financial resources, inadequate infrastructure, and restricted access to technology may exacerbate communication barriers and complicate the harmonization of healthcare practices across regions [13,61]. Those countries or regions often lack reliable mechanisms for secure data sharing and effective coordination among stakeholders [9,62]. The absence of context-specific frameworks in low-resource environments makes it difficult to implement universally applicable healthcare interventions using PPIE [46]. Despite these challenges, international co-authoring and institutional collaboration hold the potential to improve research creativity, facilitate worldwide knowledge sharing, and enhance the quality of research outcomes [33]. International collaborative efforts are also beneficial for balancing research resources and fostering the equitable development of PPIE in healthcare interventions across various regions [2].
Further research into overcoming these barriers and promoting more active global collaborations is highly encouraged. It is recommended from an actionable perspective that international collaboration in PPIE healthcare intervention research be strengthened by establishing global frameworks that promote equitable resource sharing, inclusive governance structures, and open access platforms for data and knowledge exchange. Interdisciplinary networks should be developed to foster trust, mutual learning, and the co-creation of research agendas across regions. For under-represented regions, targeted global funding mechanisms are needed to address systemic disparities through investments in interventional research programs utilizing PPIE, training programs for PPIE implementation, and sustainable access to digital tools. Furthermore, establishing monitoring and evaluation mechanisms to assess the impact of collaborative PPIE initiatives would enhance accountability, support continuous improvement, and ensure the long-term sustainability of international collaborations.
5.2. Leading and Emerging Countries
For the most productive countries, our results indicate that the USA and the UK are leading with high numbers of publications, contributing authors, influential authors, and institutions. Key authors, such as Albert Bandura in the USA and Susan Michie and Trisha Greenhalgh in the UK, have substantially contributed to these studies by promoting patient autonomy, informed choice, and the value of patients’ and the public’s preferences [21,64,65]. Major organizations like the UK’s Medical Research Council and the National Institute for Health and Care Research, along with the USA’s Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute, collectively support the use of PPIE in research [66,67]. Researchers are encouraged to adopt advanced PPIE techniques from these leaders and seek collaborative opportunities for international studies.
For the most active countries, recently, Asian countries, including Singapore, Malaysia, and Saudi Arabia, have been notably identified, likely driven by increased healthcare funding, supportive government policies, and growing recognition of PPIE’s value in healthcare interventions [68,69]. For instance, Saudi Arabia’s Vision 2030 aims to improve health and quality of life, enhance healthcare delivery and accountability, and increase the value of care by managing costs and improving outcomes [70]. These initiatives highlight PPIE’s potential to effectively utilize collective knowledge and resources to tackle complex health challenges. In Singapore, the focus on PPIE in healthcare research is influenced by global trends, demographic shifts, and practical benefits [68]. Inspired by successful PPIE cases in Western countries, Singaporean researchers are striving to improve the relevance and effectiveness of healthcare interventions. PPIE in Singapore is expected to improve research recruitment and retention, shift ethical considerations by treating participants as partners, and respond to the challenges of an aging population, ultimately optimizing the impact and reducing waste in healthcare interventions [68,71]. However, China was notably absent from the list of productive and active countries in this study despite being one of the most prolific countries in overall research publication and having the largest population globally [72]. Investigating PPIE in China, with its vast research output and huge population, may provide unique insights into the use of PPIE across varied demographic and cultural contexts, thereby enriching global healthcare intervention practices.
5.3. Key Research Themes and Trends
Our results also show the top ten research themes identified, among which “coronary heart disease”, “mental illness”, and “obesity prevention” are recognized as key health issues. This suggests that PPIE was much more frequently studied and examined in these contexts compared to others. Coronary heart disease is complex and chronic, often requiring long-term management and lifestyle changes. PPIE improves patient adherence by aligning interventions with patients’ values, preferences, and conditions, while ensuring patient-centered, relevant, and applicable strategies to promote prevention, support lifestyle modifications, and manage the disease [51]. Mental health interventions often require a high degree of personalization to be effective [53,73]. PPIE allows for the incorporation of patient experiences and preferences, which is crucial for tailoring interventions to address the unique needs of individuals with mental illnesses. Involving patients in the design and delivery of mental health services helps reduce stigma by normalizing discussions around mental health and demonstrating that patient input is valued and essential [74]. Mental health services benefit from peer support structures, and PPIE facilitates the development of peer-led interventions which have been shown to improve engagement and outcomes in mental health care [75]. PPIE is also frequently mentioned in the context of obesity prevention. This is likely because addressing this issue often requires interventions that engage not just the child, but their entire family and community [76,77]. PPIE enables the inclusion of these critical stakeholders in developing strategies that are culturally sensitive and practically feasible, thereby improving the overall effectiveness of the interventions [78]. Effective obesity interventions rely on sustained behavioral changes, which are more likely to occur when interventions are co-designed with input from children and their families in a realistic and supportive manner [79].
“Health equity” was also highlighted as a key theme. It means eliminating avoidable differences between groups and is important for making healthcare interventions accessible and suited to the needs and preferences of the target populations [80]. PPIE aims to involve diverse populations, particularly ethnic minorities, to address the needs of under-represented and disadvantaged groups and advance health equity [52]. However, challenges include lower participation rates among disadvantaged patients and potential oversight of ethnic minorities in recruitment [52,80]. In low-resource settings, the implementation of PPIE also faces obstacles due to systemic inequities, such as insufficient funding, limited healthcare access, and social exclusion of vulnerable populations [45,46,52]. The lack of standardized guidelines and regulatory frameworks for resource-constrained settings leads to inconsistencies in PPIE practices [46]. There is a need for comprehensive regulations and guidelines to standardize PPIE practices across regions and disciplines, especially in settings with limited resources. Additionally, the study indicated a trend in healthcare intervention studies reporting PPIE toward “digital health” and “mobile health” topics, reflecting the growing reliance on digital tools to involve and engage patients and the public. Digital platforms offer unique capabilities that traditional methods lack, such as the ability to provide real-time interaction and feedback, which is essential for continuous involvement and engagement [75]. They can also reduce barriers to participation and enable patients to engage with their healthcare on their own terms [81]. This flexibility is particularly valuable for individuals with busy schedules, mobility issues, or those living in remote areas. Digital data-driven approaches enable the customization of healthcare interventions to better meet the specific needs of each involved individual [82]. Additionally, digital platforms facilitate comprehensive data collection, which can be used to monitor interventional progress, identify trends, and make adjustments to interventions [83].
5.4. Limitations
Limitations arise from focusing on high-quality articles published in English and indexed in the WoS, which may not represent findings from non-English publications or those excluded by the WoS. We utilized BR techniques to present a clear and comprehensive bibliometric overview by analyzing a large dataset through CiteSpace. However, the extensive dataset and specific functionalities of CiteSpace might limit in-depth qualitative analyses and interpretations from diverse perspectives, such as PPIE patterns in different studies and the specific applications of PPIE across various countries. They may also limit the clarity of visualization display functions, making the figures appear unclear for more comprehensive analyses (e.g., dual map overlay). Additionally, our search strategy for identifying healthcare intervention-related studies reporting PPIE was based on our definition and understanding of PPIE and interventional studies. Using different search terms might have produced different results. Future studies could address these limitations by diversifying the databases used, such as incorporating Scopus, PubMed, or regional databases, to capture a broader range of publications, including non-English and region-specific studies that may provide unique perspectives on PPIE practices. Additionally, integrating qualitative methodologies, such as thematic analysis or case studies, could offer deeper insights into the contextual factors influencing PPIE implementation and the specific challenges faced in different healthcare systems and cultural settings. Employing iterative and inclusive search strategies, such as refining search terms through stakeholder consultation or pilot searches, would help account for variability in how PPIE and interventional studies are defined and reported across disciplines and regions.
6. Conclusions
This paper addresses a research gap by presenting a global and up-to-date BR of healthcare intervention-related studies reporting PPIE. It identifies key bibliometric features, themes, and trends within the field, providing researchers and healthcare professionals with a clearer understanding of the trajectory of relevant studies. The paper highlights potential areas for future research and practical application, offering guidance for advancing the field. Researchers can refer to this study and its findings to identify relevant focal points for future publications, seek collaborations across authors, institutions, and countries, and properly use PPIE in different research stages. Given the recent increase in research outputs from Asia, where fewer countries have historically been identified as productive in PPIE-related research, it is recommended to develop strategies or policies to support Asian researchers in actively engaging, implementing, and promoting PPIE. Frontline healthcare professionals are expected to utilize PPIE properly to optimize the development, design, and implementation of healthcare interventions. For example, given the emerging trend of digital and mobile health, practitioners can leverage digital tools such as mobile health apps, virtual platforms, and telehealth systems to facilitate more effective and inclusive PPIE. These tools can streamline communication, improve accessibility to underserved populations, and provide real-time feedback mechanisms, enabling practitioners to capture diverse patient perspectives more systematically and efficiently. For instance, mobile health apps can be used to collect patient-reported outcomes or preferences, while virtual platforms can host consultations or focus groups that accommodate participants from geographically remote areas. Policymakers can create enabling environments that support the adoption of PPIE in digital health, which may include embedding PPIE utilization into regional digital health strategies and allocating targeted funding to incentivize PPIE-driven projects. They can also establish training programs and technical support systems to empower healthcare practitioners to use digital tools effectively, while promoting data security and upholding ethical standards in PPIE activities. Lastly, this study offers healthcare professionals and researchers opportunities to collaborate with prominent scholars in developing evidence-based findings for using PPIE in research and practices related to healthcare interventions.
Conceptualization, W.L., Y.L., J.M., M.L., K.Y.H., J.L. and J.Y.; Methodology, W.L., Y.L., J.M., M.L., K.Y.H., J.L. and J.Y.; Software, W.L.; Validation, W.L., Y.L. and M.L.; Formal analysis, W.L.; Investigation, W.L. and M.L.; Resources, W.L., Y.L. and J.Y.; Data curation, W.L. and M.L.; Writing—original draft, W.L.; Writing—review & editing, Y.L., J.M., M.L., K.Y.H., J.L. and J.Y.; Visualization, W.L.; Supervision, Y.L. and J.Y.; Project administration, Y.L. and J.Y. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data will be shared upon request to the first author.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Search strategy and inclusion criteria of healthcare intervention-related studies reporting PPIE in the WoS.
Enlarge this image.Figure 3. Author collaboration network. Note: This figure depicts the collaborative relationships among authors contributing to PPIE healthcare intervention research. Each node represents an author, with the node size proportional to the number of publications authored. Links between nodes signify co-authorship relationships, where thicker and shorter links indicate stronger and more frequent collaboration. The listed authors reflect their influential roles in fostering international collaboration in PPIE healthcare intervention research and serve as key connectors, bridging multiple clusters and facilitating cross-disciplinary research efforts.
Enlarge this image.Figure 4. Country collaborative network. Note: This figure illustrates the collaborative relationships between countries involved in PPIE healthcare intervention research. Each node represents a country/region, with the node size corresponding to the volume of publications produced by that country/region. Links between nodes indicate collaborative relationships. The USA, England, Canada, and Australia are prominent nodes, highlighting their central and active roles in driving international collaborations. Smaller nodes, such as Belgium, Denmark, and Switzerland, indicate contributions from countries with fewer publications but are important collaborative roles within the network.
Enlarge this image.Figure 5. Institutional collaboration network. Note: This figure illustrates the collaborative relationships between institutions involved in PPIE healthcare intervention research. Each node represents an institution, with node size proportional to the institution’s publication output in the dataset. The links between nodes indicate collaborative relationships, where thicker and shorter links represent stronger and closer collaborations. Network clustering patterns reveal distinct groups of institutions with denser interconnections, which likely arise from factors such as geographic proximity, focused research agendas, or longstanding collaborative networks. The patterns highlight the varying levels of integration among institutions, with some forming strong collaborative hubs while others remain more peripheral.
Enlarge this image.Figure 6. Top 20 countries/regions with the strongest citation bursts. Note: Light blue bars represent the period before relevant articles from a country/region were published, while dark blue bars indicate the onset of publication. Red segments mark the start and end of a citation burst period.
Enlarge this image.Figure 7. Top 25 keywords with the strongest citation bursts. Note: Light blue bars represent the period before the keywords appeared, while dark blue bars indicate the onset of keyword appearance. Red segments mark the start and end of a keyword citation burst period.
Data analysis methods for each bibliometric feature.
| Bibliometric Features | Analysis Used | Purpose |
|---|---|---|
| Yearly Publications, Authors, Journals, and Countries | WoS bibliometric and descriptive analysis function | To identify each distribution in past decades |
| Influential Authors/Journals/References | Co-citation analysis through CiteSpace [ | To identify citation counts for academic influence evaluation |
| Collaborations Among Authors And Countries | Network visualization through CiteSpace [ | To identify productive authors and countries in global collaborations |
| Betweenness centrality analysis through CiteSpace [ | To identify active cross-country collaboration and country influence | |
| Thematic Analysis | Co-citation cluster analysis through CiteSpace [ | To measure thematic similarities between articles, with titles, keywords, and abstracts analyzed for theme identification |
| Thematic Labeling | Log-likelihood ratio method through CiteSpace [ | To provide precise thematic labeling by derivation from article titles, keywords, and abstracts |
| Trend Analysis for Countries | Citation bursts analysis through CiteSpace [ | To reveal past developmental trends in country participation based on periods of significantly increased research outputs and to identify recently active countries |
| Trend Analysis for Themes | Keyword bursts (using both author keywords and keywords plus from WoS) through CiteSpace [ | To identify evolving thematic trends and areas of growing academic interest |
Top ten journals and authors regarding the number of published research articles.
| Ranking | Journal Title | No. of Articles (%) | Authors | No. of Articles (%) |
|---|---|---|---|---|
| 1 | BMJ Open | 417 (3.93) | Wells Kenneth | 32 (0.30) |
| 2 | BMC Health Services Research | 244 (2.30) | Tucker Joseph | 24 (0.23) |
| 3 | Health Expectations | 233 (2.19) | Lovell Karin | 23 (0.22) |
| 4 | PLOS ONE | 217 (2.04) | Mullins Daniel | 21 (0.20) |
| 5 | BMC Public Health | 204 (1.92) | Montori Victor Manuel | 21 (0.20) |
| 6 | Journal of Medical Internet Research | 175 (1.65) | Chaboyer Wendy | 21 (0.20) |
| 7 | International Journal of Environmental Research and Public Health | 156 (1.47) | Lucy Yardley | 20 (0.19) |
| 8 | Trials | 141 (1.33) | Chung Bowen | 19 (0.18) |
| 9 | JMIR Formative Research | 138 (1.30) | Tang Lingqi | 19 (0.18) |
| 10 | Patient education and counseling | 129 (1.21) | Miranda Jeanne/Petrou Stavros/Mcelfish Pearl/Staniszewska Sophie/Bower Peter | 18 (0.17) |
Top ten authors based on author co-citation network analysis.
| Rank | Number of Citations | Author |
|---|---|---|
| 1 | 1727 | World Health Organization |
| 2 | 1085 | Braun Vittoria |
| 3 | 498 | Susan Michie |
| 4 | 392 | Albert Bandura |
| 5 | 389 | Trisha Greenhalgh |
| 6 | 372 | Tong Allison |
| 7 | 318 | Kroenke Kurt |
| 8 | 309 | Laura Damschroder |
| 9 | 303 | Glasgow Robert |
| 10 | 284 | Patton Michael |
Top ten references based on reference co-citation network analysis.
| Rank | Number of Citations | Article | Journal |
|---|---|---|---|
| 1 | 3502 | Moore et al. [ | BMJ |
| 2 | 2393 | Skivington et al. [ | BMJ |
| 3 | 1954 | Stacey et al. [ | Cochrane Database of Systematic Reviews |
| 4 | 809 | Brett et al. [ | Health Expectations |
| 5 | 755 | Alicia et al. [ | BMJ Open |
| 6 | 604 | Perski et al. [ | Translational Behavioral Medicine |
| 7 | 585 | Yardley et al. [ | American Journal of Preventive Medicine |
| 8 | 575 | Staniszewska et al. [ | BMJ |
| 9 | 515 | Slattery et al. [ | Health Research Policy and Systems |
| 10 | 370 | Greenhalgh et al. [ | Health Expectations |
Top ten journals based on journal co-citation network analysis.
| Rank | Number of Citations | Journals |
|---|---|---|
| 1 | 2791 | Plos One |
| 2 | 2668 | BMJ |
| 3 | 2656 | Lancet |
| 4 | 2379 | JAMA |
| 5 | 2181 | Social Science & Medicine |
| 6 | 2019 | BMJ Open |
| 7 | 1886 | BMC Health Services Research |
| 8 | 1824 | BMC Public Health |
| 9 | 1716 | American Journal of Public Health |
| 10 | 1643 | Journal of General Internal Medicine |
Top ten countries regarding the number of published research articles and centrality values.
| Rank by No. of Articles | Country | No. of Articles (%) | Rank by Betweenness Centrality Value | Country/ No. of Articles | Betweenness Centrality Value * |
|---|---|---|---|---|---|
| 1 | USA | 4327 (40.73) | 1 | Lebanon (12) | 0.89 |
| 2 | England | 2390 (22.50) | 2 | South Africa (300) | 0.69 |
| 3 | Australia | 1362 (12.82) | 3 | India (225) | 0.51 |
| 4 | Canada | 994 (9.36) | 4 | Portugal (72) | 0.50 |
| 5 | Netherlands | 557 (5.24) | 5 | Belgium (210) | 0.49 |
| 6 | Germany | 357 (3.36) | 6 | Argentina (20) | 0.46 |
| 7 | Sweden | 324 (3.05) | 7 | Chile (13) | 0.45 |
| 8 | South Africa | 318 (2.99) | 8 | Singapore (55) | 0.43 |
| 9 | Scotland | 311 (2.93) | 9 | Nepal (25) | 0.43 |
| 10 | Denmark | 268 (2.52) | 10 | Slovenia (4) | 0.39 |
* Note: Betweenness centrality is a network analysis metric that measures the extent to which a node (e.g., a country) acts as a bridge between other nodes by lying on the shortest paths connecting them [
Top ten institutions regarding the number of published research articles.
| Ranking | Institution | No. of Articles (%) | Country |
|---|---|---|---|
| 1 | University of London | 846 (7.96) | UK |
| 2 | University of California System | 551 (5.19) | US |
| 3 | Harvard University | 361 (3.40) | US |
| 4 | University College London | 325 (3.06) | UK |
| 5 | University of Toronto | 295 (2.78) | Canada |
| 6 | Kings College London | 293 (2.76) | UK |
| 7 | Johns Hopkins University | 288 (2.71) | US |
| 8 | University of Sydney | 272 (2.56) | Australia |
| 9 | University of North Carolina | 267 (2.51) | US |
| 10 | US Department of Veterans Affairs | 258 (2.43) | US |
Top ten research areas regarding the number of published research articles identified by the WoS.
| Ranking | Research Area | No. of Articles (%) |
|---|---|---|
| 1 | Public Environmental Occupation Health | 2802 (26.38) |
| 2 | Health Care Sciences Services | 2522 (23.74) |
| 3 | General Internal Medicine | 1153 (10.85) |
| 4 | Psychiatry | 757 (7.13) |
| 5 | Medical Informatics | 751 (7.07) |
| 6 | Psychology | 711 (6.69) |
| 7 | Nursing | 547 (5.15) |
| 8 | Rehabilitation | 504 (4.74) |
| 9 | Research Experimental Medicine | 379 (3.57) |
| 10 | Environmental Sciences Ecology | 300 (2.82) |
Theme identification by titles, keywords, and abstracts in healthcare intervention-related studies reporting PPIE.
| Cluster | Size | Silhouette | Label (Log-Likelihood Ratio) (LLR) | Average Year | Top Terms | Representative Article |
|---|---|---|---|---|---|---|
| 1 | 89 | 0.898 | Community participation | 2004 | impact; outcome; program; prevention; quality | Arnett et al. (2019). 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines [ |
| 2 | 50 | 0.877 | Health equity | 2019 | mental health; community engagement; community-based participatory research | Simon et al. (2020). Patient and family engaged care: an essential element of health equity. NAM perspectives, 2020 [ |
| 3 | 49 | 0.859 | Coronary heart disease | 2017 | physical activity; | Arnett et al. (2019). 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines [ |
| 4 | 48 | 0.842 | Web-based patient empowerment | 2018 | involvement; health literacy; | Geerling et al. (2022). A web-based positive psychology app for patients with bipolar disorder: Development study [ |
| 5 | 48 | 0.924 | Mental illness | 2016 | impact; outcome; people; services; schizophrenia | Granlund et al. (2021). Definitions and operationalization of mental health problems, wellbeing and participation constructs in children with NDD: distinctions and clarifications [ |
| 6 | 47 | 0.901 | Obesity prevention | 2005 | care; intervention; children; program; qualitative research | Whelan et al. (2022). Reflexive evidence and systems interventions to prevention obesity and non-communicable disease (RESPOND): protocol and baseline outcomes for a stepped-wedge cluster-randomised prevention trial [ |
| 7 | 46 | 0.92 | Intervention mapping | 2014 | participation; risk; depression; barriers; adults | Eaton et al. (2021). Training Peers to Ease Hospital Discharge: A Community–Clinical Partnership in Complex HIV Care [ |
| 8 | 46 | 0.879 | Healthy behavior | 2018 | randomized controlled trial; digital health; mobile phone; cognitive behavioral therapy | Pozuelo et al. (2023). User-Centered Design of a Gamified Mental Health App for Adolescents in Sub-Saharan Africa: Multicycle Usability Testing Study [ |
| 9 | 46 | 0.906 | Reporting guidance | 2018 | people; services; implementation science; technology; life | Husereau et al. (2022). Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations [ |
| 10 | 40 | 0.883 | Special populations network | 2010 | health; prevention; quality; primary care; community participation | Eaton et al. (2021). Training Peers to Ease Hospital Discharge: A Community–Clinical Partnership in Complex HIV Care [ |
References
1. Williams, E. How does co-production differ from patient and public involvement (PPI)?. Br. J. Gen. Pract.; 2023; 73, 737. [DOI: https://dx.doi.org/10.3399/bjgp23X735645]
2. Slattery, P.; Saeri, A.K.; Bragge, P. Research co-design in health: A rapid overview of reviews. Health Res. Policy Syst.; 2020; 18, pp. 1-13. [DOI: https://dx.doi.org/10.1186/s12961-020-0528-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32046728]
3. Vinnicombe, S.; Bianchim, M.S.; Noyes, J. A review of reviews exploring patient and public involvement in population health research and development of tools containing best practice guidance. BMC Public Health; 2023; 23, 1271. [DOI: https://dx.doi.org/10.1186/s12889-023-15937-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37391764]
4. NIHR. Briefing Notes for Researchers-Public Involvement in NHS, Health and Social Care Research; NIHR, United Kingdom: 2024. Available online: https://www.nihr.ac.uk/briefing-notes-researchers-public-involvement-nhs-health-and-social-care-research (accessed on 26 January 2025).
5. Louise, L.; Annette, B. Drawing straight lines along blurred boundaries: Qualitative research, patient and public involvement in medical research, co-production and co-design. Evid. Policy; 2019; 15, pp. 409-421. [DOI: https://dx.doi.org/10.1332/174426419X15552999451313]
6. Crawford, M.J.; Rutter, D.; Manley, C.; Weaver, T.; Bhui, K.; Fulop, N.; Tyrer, P. Systematic review of involving patients in the planning and development of health care. BMJ; 2002; 325, 1263. [DOI: https://dx.doi.org/10.1136/bmj.325.7375.1263] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12458240]
7. The University of Manchester. Patient and Public Involvement: Social Responsibility. Available online: https://www.manchester.ac.uk/discover/social-responsibility/better-health/patient-and-public-involvement-and-engagement/ (accessed on 30 May 2024).
8. National Institute for Health and Care Research. Patient and public involvement and Engagement Resource Pack. Available online: https://www.nihr.ac.uk/documents/patient-and-public-involvement-and-engagement-resource-pack/31218 (accessed on 30 May 2024).
9. Health Data Research UK. Involving and Engaging Patients and the Public 2023. Available online: https://www.hdruk.ac.uk/about-us/patient-and-public-involvement-and-engagement/ (accessed on 30 May 2024).
10. Seeralan, T.; Härter, M.; Koschnitzke, C.; Scholl, M.; Kohlmann, S.; Lehmann, M.; Eisele, M.; Braunschneider, L.; Marx, G.; Scherer, M. et al. Patient involvement in developing a patient-targeted feedback intervention after depression screening in primary care within the randomized controlled trial GET. FEEDBACK. GP. Health Expect.; 2021; 24, pp. 95-112. [DOI: https://dx.doi.org/10.1111/hex.13039]
11. Rai, T.; Morton, K.; Roman, C.; Doogue, R.; Rice, C.; Williams, M.; Schwartz, C.; Velardo, C.; Tarassenko, L.; Yardley, L. et al. Optimizing a digital intervention for managing blood pressure in stroke patients using a diverse sample: Integrating the person-based approach and patient and public involvement. Health Expect.; 2021; 24, pp. 327-340. [DOI: https://dx.doi.org/10.1111/hex.13173] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33316120]
12. Andrews, L.M.; Allen, H.; Sheppard, Z.A.; Baylis, G.; Wainwright, T.W. More than just ticking a box… how patient and public involvement improved the research design and funding application for a project to evaluate a cycling intervention for hip osteoarthritis. Res. Involv. Engagem.; 2015; 1, 13. [DOI: https://dx.doi.org/10.1186/s40900-015-0013-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29062501]
13. Skivington, K.; Matthews, L.; Simpson, S.A.; Craig, P.; Baird, J.; Blazeby, J.M.; Boyd, K.A.; Craig, N.; French, D.P.; McIntosh, E. et al. A new framework for developing and evaluating complex interventions: Update of Medical Research Council guidance. BMJ; 2021; 374, n2061. [DOI: https://dx.doi.org/10.1136/bmj.n2061]
14. Alicia, O.; Croot, L.; Duncan, E.; Rousseau, N.; Sworn, K.; Turner, K.M.; Yardley, L.; Hoddinott, P. Guidance on how to develop complex interventions to improve health and healthcare. BMJ Open; 2019; 9, e029954.
15. Moore, G.F.; Audrey, S.; Barker, M.; Bond, L.; Bonell, C.; Hardeman, W.; Moore, L.; O’cathain, A.; Tinati, T.; Wight, D. et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ; 2015; 350, h1258. [DOI: https://dx.doi.org/10.1136/bmj.h1258]
16. Brett, J.; Staniszewska, S.; Mockford, C.; Herron-Marx, S.; Hughes, J.; Tysall, C.; Suleman, R. Mapping the impact of patient and public involvement on health and social care research: A systematic review. Health Expect.; 2014; 17, pp. 637-650. [DOI: https://dx.doi.org/10.1111/j.1369-7625.2012.00795.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22809132]
17. Welch, J.; Fournier, A. Patient engagement through informed nurse caring. Int. J. Hum. Caring; 2018; 22, pp. 10-19. [DOI: https://dx.doi.org/10.20467/1091-5710.22.1.pg5]
18. Jerofke-Owen, T.A.; Tobiano, G.; Eldh, A.C. Patient engagement, involvement, or participation—Entrapping concepts in nurse-patient interactions: A critical discussion. Nurs. Inq.; 2023; 30, e12513. [DOI: https://dx.doi.org/10.1111/nin.12513] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35871476]
19. Snyder, H.; Engström, J. The antecedents, forms and consequences of patient involvement: A narrative review of the literature. Int. J. Nurs. Stud.; 2016; 53, pp. 351-378. [DOI: https://dx.doi.org/10.1016/j.ijnurstu.2015.09.008]
20. Hyde, C.; Dunn, K.M.; Higginbottom, A.; Chew-Graham, C.A. Process and impact of patient involvement in a systematic review of shared decision making in primary care consultations. Health Expect.; 2017; 20, pp. 298-308. [DOI: https://dx.doi.org/10.1111/hex.12458] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27170213]
21. Greenhalgh, T.; Hinton, L.; Finlay, T.; Macfarlane, A.; Fahy, N.; Clyde, B.; Chant, A. Frameworks for supporting patient and public involvement in research: Systematic review and co-design pilot. Health Expect.; 2019; 22, pp. 785-801. [DOI: https://dx.doi.org/10.1111/hex.12888] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31012259]
22. Mallett, R.; Hagen-Zanker, J.; Slater, R.; Duvendack, M. The benefits and challenges of using systematic reviews in international development research. J. Dev. Eff.; 2012; 4, pp. 445-455. [DOI: https://dx.doi.org/10.1080/19439342.2012.711342]
23. Pae, C.-U. Why systematic review rather than narrative review?. Psychiatry Investig.; 2015; 12, 417. [DOI: https://dx.doi.org/10.4306/pi.2015.12.3.417] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26207140]
24. Fusco, F.; Marsilio, M.; Guglielmetti, C. Co-production in health policy and management: A comprehensive bibliometric review. BMC Health Serv. Res.; 2020; 20, 504. [DOI: https://dx.doi.org/10.1186/s12913-020-05241-2]
25. Rodrigues, S.P.; Van Eck, N.; Waltman, L.; Jansen, F.W. Mapping patient safety: A large-scale literature review using bibliometric visualisation techniques. BMJ Open; 2014; 4, e004468. [DOI: https://dx.doi.org/10.1136/bmjopen-2013-004468] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24625640]
26. Kamdem, J.P.; Duarte, A.E.; Lima, K.R.R.; Rocha, J.B.T.; Hassan, W.; Barros, L.M.; Roeder, T.; Tsopmo, A. Research trends in food chemistry: A bibliometric review of its 40 years anniversary (1976–2016). Food Chem.; 2019; 294, pp. 448-457. [DOI: https://dx.doi.org/10.1016/j.foodchem.2019.05.021] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31126486]
27. Aggarwal, R.; Ranganathan, P. Study designs: Part 4–interventional studies. Perspect. Clin. Res.; 2019; 10, pp. 137-139. [DOI: https://dx.doi.org/10.4103/picr.PICR_91_19] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31404185]
28. Glasser, S.P. Introduction to clinical research and study designs. Essentials of Clinical Research; Springer: Berlin/Heidelberg, Germany, 2008; pp. 13-27.
29. Gao, Y.; Pan, Y.; Han, S.; Chen, W.; Jing, J.; Wang, C.; Yang, Y.; Wang, T.; Meng, X.; Zhao, X. et al. Rationale and design of a randomised double-blind 2 × 2 factorial trial comparing the effect of a 3-month intensive statin and antiplatelet therapy for patients with acute mild ischaemic stroke or high-risk TIA with intracranial or extracranial atherosclerosis (INSPIRES). Stroke Vasc. Neurol.; 2023; 8, pp. 249-258. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36707080]
30. Montazeri, A.; Mohammadi, S.M.; Hesari, P.; Ghaemi, M.; Riazi, H.; Sheikhi-Mobarakeh, Z. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): A minimum requirements. Syst. Rev.; 2023; 12, 239. [DOI: https://dx.doi.org/10.1186/s13643-023-02410-2]
31. Koo, M.; Lin, S.-C. An analysis of reporting practices in the top 100 cited health and medicine-related bibliometric studies from 2019 to 2021 based on a proposed guidelines. Heliyon; 2023; 9, e16780. [DOI: https://dx.doi.org/10.1016/j.heliyon.2023.e16780] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37292336]
32. Birkle, C.; Pendlebury, D.A.; Schnell, J.; Adams, J. Web of Science as a data source for research on scientific and scholarly activity. Quant. Sci. Stud.; 2020; 1, pp. 363-376. [DOI: https://dx.doi.org/10.1162/qss_a_00018]
33. Lu, W.; Ngai, C.S.B.; Yi, L. A bibliometric review of constituents, themes, and trends in online medical consultation research. Health Commun.; 2024; 39, pp. 229-243. [DOI: https://dx.doi.org/10.1080/10410236.2022.2163108]
34. Pranckutė, R. Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications; 2021; 9, 12. [DOI: https://dx.doi.org/10.3390/publications9010012]
35. Visser, M.; Van Eck, N.J.; Waltman, L. Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic. Quant. Sci. Stud.; 2021; 2, pp. 20-41. [DOI: https://dx.doi.org/10.1162/qss_a_00112]
36. MeSH. National Library of Medicine. Available online: https://www.ncbi.nlm.nih.gov/mesh/ (accessed on 12 June 2024).
37. Hoekstra, F.; Mrklas, K.J.; Khan, M.; McKay, R.C.; Vis-Dunbar, M.; Sibley, K.M.; Nguyen, T.; Graham, I.D. SCI Guiding Principles Consensus Panel Gainforth, H.L. A review of reviews on principles, strategies, outcomes and impacts of research partnerships approaches: A first step in synthesising the research partnership literature. Health Res. Policy Syst.; 2020; 18, 51. [DOI: https://dx.doi.org/10.1186/s12961-020-0544-9]
38. Rogers, M.; Bethel, A.; Boddy, K. Development and testing of a medline search filter for identifying patient and public involvement in health research. Health Inf. Libr. J.; 2017; 34, pp. 125-133. [DOI: https://dx.doi.org/10.1111/hir.12157]
39. Nan, D.; Zhao, X.; Chen, C.; Sun, S.; Lee, K.R.; Kim, J.H. Bibliometric Analysis on ChatGPT Research with CiteSpace. Information; 2025; 16, 38. [DOI: https://dx.doi.org/10.3390/info16010038]
40. Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol.; 2006; 57, pp. 359-377. [DOI: https://dx.doi.org/10.1002/asi.20317]
41. Zhao, X.; Nan, D.; Chen, C.; Zhang, S.; Che, S.; Kim, J.H. Bibliometric study on environmental, social, and governance research using CiteSpace. Front. Environ. Sci.; 2023; 10, 1087493. [DOI: https://dx.doi.org/10.3389/fenvs.2022.1087493]
42. Sun, W.; Song, J.; Dong, X.; Kang, X.; He, B.; Zhao, W.; Li, Z.; Feng, Z.; Chen, X. Bibliometric and visual analysis of transcranial direct current stimulation in the web of science database from 2000 to 2022 via CiteSpace. Front. Hum. Neurosci.; 2022; 16, 1049572. [DOI: https://dx.doi.org/10.3389/fnhum.2022.1049572] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36530203]
43. Wang, Z.; Ma, D.; Pang, R.; Xie, F.; Zhang, J.; Sun, D. Research progress and development trend of social media big data (smbd): Knowledge mapping analysis based on citespace. ISPRS Int. J. Geo-Inf.; 2020; 9, 632. [DOI: https://dx.doi.org/10.3390/ijgi9110632]
44. Zou, H.; Hyland, K. Stance in academic blogs and three-minute theses. Int. J. Appl. Linguist.; 2022; 32, pp. 225-240. [DOI: https://dx.doi.org/10.1111/ijal.12411]
45. Shi, X.; Wang, S.; Wu, Y.; Li, Q.; Zhang, T.; Min, K.; Feng, D.; Liu, M.; Wei, J.; Zhu, L. et al. A bibliometric analysis of the innate immune DNA sensing cGAS-STING pathway from 2013 to 2021. Front. Immunol.; 2022; 13, 916383. [DOI: https://dx.doi.org/10.3389/fimmu.2022.916383]
46. Staniszewska, S.; Brett, J.; Simera, I.; Seers, K.; Mockford, C.; Goodlad, S.; Altman, D.G.; Moher, D.; Barber, R.; Denegri, S. et al. GRIPP2 reporting checklists: Tools to improve reporting of patient and public involvement in research. BMJ; 2017; 358, j3453. [DOI: https://dx.doi.org/10.1136/bmj.j3453] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28768629]
47. Perski, O.; Blandford, A.; West, R.; Michie, S. Conceptualising engagement with digital behaviour change interventions: A systematic review using principles from critical interpretive synthesis. Transl. Behav. Med.; 2017; 7, pp. 254-267. [DOI: https://dx.doi.org/10.1007/s13142-016-0453-1]
48. Yardley, L.; Spring, B.J.; Riper, H.; Morrison, L.G.; Crane, D.H.; Curtis, K.; Merchant, G.C.; Naughton, F.; Blandford, A. Understanding and promoting effective engagement with digital behavior change interventions. Am. J. Prev. Med.; 2016; 51, pp. 833-842. [DOI: https://dx.doi.org/10.1016/j.amepre.2016.06.015]
49. Stacey, D.; Légaré, F.; Lewis, K.; Barry, M.J.; Bennett, C.L.; Eden, K.B.; Holmes-Rovner, M.; Llewellyn-Thomas, H.; Lyddiatt, A.; Thomson, R. et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst. Rev.; 2017; 2017, CD001431. [DOI: https://dx.doi.org/10.1002/14651858.CD001431.pub5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28402085]
50. Su, W.; Sun, J.; Shimizu, K.; Kadota, K. TCC-GUI: A Shiny-based application for differential expression analysis of RNA-Seq count data. BMC Res. Notes; 2019; 12, 133. [DOI: https://dx.doi.org/10.1186/s13104-019-4179-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30867032]
51. Arnett, D.K.; Blumenthal, R.S.; Albert, M.A.; Buroker, A.B.; Goldberger, Z.D.; Hahn, E.J.; Himmelfarb, C.D.; Khera, A.; Lloyd-Jones, D.; McEvoy, J.W. et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: Executive summary: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol.; 2019; 74, pp. 1376-1414. [DOI: https://dx.doi.org/10.1016/j.jacc.2019.03.009]
52. Simon, M.; Baur, C.; Guastello, S.; Ramiah, K.; Tufte, J.; Wisdom, K.; Johnston-Fleece, M.; Cupito, A.; Anise, A. Patient and family engaged care: An essential element of health equity. NAM Perspect.; 2020; 2020, [DOI: https://dx.doi.org/10.31478/202007a]
53. Geerling, B.; Kelders, S.M.; Stevens, A.W.; Kupka, R.W.; Bohlmeijer, E.T. A web-based positive psychology app for patients with bipolar disorder: Development study. JMIR Form. Res.; 2022; 6, e39476. [DOI: https://dx.doi.org/10.2196/39476] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35946327]
54. Granlund, M.; Imms, C.; King, G.; Andersson, A.K.; Augustine, L.; Brooks, R.; Danielsson, H.; Gothilander, J.; Ivarsson, M.; Lundqvist, L.-O. et al. Definitions and operationalization of mental health problems, wellbeing and participation constructs in children with NDD: Distinctions and clarifications. Int. J. Environ. Res. Public Health; 2021; 18, 1656. [DOI: https://dx.doi.org/10.3390/ijerph18041656] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33572339]
55. Whelan, J.; Hayward, J.; Nichols, M.; Brown, A.D.; Orellana, L.; Brown, V.; Becker, D.; Bell, C.; Swinburn, B.; Peeters, A. et al. Reflexive evidence and systems interventions to prevention obesity and non-communicable disease (RESPOND): Protocol and baseline outcomes for a stepped-wedge cluster-randomised prevention trial. BMJ Open; 2022; 12, e057187. [DOI: https://dx.doi.org/10.1136/bmjopen-2021-057187]
56. Eaton, A.D.; Carusone, S.C.; Ceranto, A.; Craig, S.L.; Busch, A.; McCullagh, J.W. Training Peers to Ease Hospital Discharge: A Community–Clinical Partnership in Complex HIV Care. Prog. Community Health Partnersh. Res. Educ. Action; 2021; 15, pp. 225-260. [DOI: https://dx.doi.org/10.1353/cpr.2021.0024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34248066]
57. Pozuelo, J.R.; Moffett, B.D.; Davis, M.; Stein, A.; Cohen, H.; Craske, M.G.; Maritze, M.; Makhubela, P.; Nabulumba, C.; Sikoti, D. et al. User-Centered Design of a Gamified Mental Health App for Adolescents in Sub-Saharan Africa: Multicycle Usability Testing Study. JMIR Form. Res.; 2023; 7, e51423. [DOI: https://dx.doi.org/10.2196/51423] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38032691]
58. Husereau, D.; Drummond, M.; Augustovski, F.; de Bekker-Grob, E.; Briggs, A.H.; Carswell, C.; Caulley, L.; Chaiyakunapruk, N.; Greenberg, D.; Loder, E. et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: Updated reporting guidance for health economic evaluations. MDM Policy Pract.; 2022; 7, 23814683211061097. [DOI: https://dx.doi.org/10.1177/23814683211061097] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35036563]
59. World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. Jama; 2013; 310, pp. 2191-2194. [DOI: https://dx.doi.org/10.1001/jama.2013.281053]
60. Green, B.N.; Johnson, C.D. Interprofessional collaboration in research, education, and clinical practice: Working together for a better future. J. Chiropr. Educ.; 2015; 29, pp. 1-10. [DOI: https://dx.doi.org/10.7899/JCE-14-36] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25594446]
61. Haak, M.; Himmelsbach, I.; Granbom, M.; Löfqvist, C. Cross-national and multi-language qualitative research: Challenges and recommendations. Br. J. Occup. Ther.; 2013; 76, pp. 333-336. [DOI: https://dx.doi.org/10.4276/030802213X13729279115059]
62. Pratt, R.; Gyllstrom, B.; Gearin, K.; Lange, C.; Hahn, D.; Baldwin, L.-M.; VanRaemdonck, L.; Nease, D.; Zahner, S. Identifying barriers to collaboration between primary care and public health: Experiences at the local level. Public Health Rep.; 2018; 133, pp. 311-317. [DOI: https://dx.doi.org/10.1177/0033354918764391] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29614236]
63. Totzeck, C.; van der Meer, A.S.; Christiansen, H.; Durlach, F.; Li Sanchez, K.; Schneider, S. Systematic Review: Patient and Public Involvement of Children and Young People in Mental Health Research. Clin. Child Fam. Psychol. Rev.; 2024; 27, pp. 257-274. [DOI: https://dx.doi.org/10.1007/s10567-024-00470-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38402358]
64. Bandura, A. Toward a psychology of human agency. Perspect. Psychol. Sci.; 2006; 1, pp. 164-180. [DOI: https://dx.doi.org/10.1111/j.1745-6916.2006.00011.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26151469]
65. Michie, S.; Miles, J.; Weinman, J. Patient-centredness in chronic illness: What is it and does it matter?. Patient Educ. Couns.; 2003; 51, pp. 197-206. [DOI: https://dx.doi.org/10.1016/S0738-3991(02)00194-5]
66. Domecq, J.P.; Prutsky, G.; Elraiyah, T.; Wang, Z.; Nabhan, M.; Shippee, N.; Brito, J.P.; Boehmer, K.; Hasan, R.; Firwana, B. et al. Patient engagement in research: A systematic review. BMC Health Serv. Res.; 2014; 14, 89. [DOI: https://dx.doi.org/10.1186/1472-6963-14-89] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24568690]
67. Bierman, A.S.; Wang, J.; O’Malley, P.G.; Moss, D.K. Transforming care for people with multiple chronic conditions: Agency for Healthcare Research and Quality’s research agenda. Health Serv. Res.; 2021; 56, (Suppl. S1), 973. [DOI: https://dx.doi.org/10.1111/1475-6773.13863]
68. Luna Puerta, L.; Bartlam, B.; Smith, H.E. Researchers’ perspectives on public involvement in health research in Singapore: The argument for a community-based approach. Health Expect.; 2019; 22, pp. 666-675. [DOI: https://dx.doi.org/10.1111/hex.12915] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31322811]
69. AlHaqwi, A.I.; AlDrees, T.M.; AlRumayyan, A.; AlFarhan, A.I.; Alotaibi, S.S.; AlKhashan, H.I.; Badri, M. Shared clinical decision making: A Saudi Arabian perspective. Saudi Med. J.; 2015; 36, 1472. [DOI: https://dx.doi.org/10.15537/smj.2015.12.13682]
70. Al-Sahli, B.; Eldali, A.; Aljuaid, M.; Al-Surimi, K. Person-centered care in a tertiary hospital through patient’s eyes: A cross-sectional study. Patient Prefer. Adherence; 2021; 15, pp. 761-773. [DOI: https://dx.doi.org/10.2147/PPA.S286237]
71. Luna Puerta, L.; Smith, H.E. The “PPI Hawker”: An innovative method for patient and public involvement (PPI) in health research. Res. Involv. Engagem.; 2020; 6, 31. [DOI: https://dx.doi.org/10.1186/s40900-020-00205-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32551135]
72. Tollefson, J. China declared world’s largest producer of scientific articles. Nature; 2018; 553, pp. 390-391. [DOI: https://dx.doi.org/10.1038/d41586-018-00927-4]
73. Ng, M.Y.; Weisz, J.R. Annual research review: Building a science of personalized intervention for youth mental health. J. Child Psychol. Psychiatry; 2016; 57, pp. 216-236. [DOI: https://dx.doi.org/10.1111/jcpp.12470] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26467325]
74. Happell, B.; Waks, S.; Bocking, J.; Horgan, A.; Manning, F.; Greaney, S.; Goodwin, J.; Scholz, B.; van der Vaart, K.J.; Allon, J. et al. ‘There’s more to a person than what’s in front of you’: Nursing students’ experiences of consumer taught mental health education. Int. J. Ment. Health Nurs.; 2019; 28, pp. 950-959. [DOI: https://dx.doi.org/10.1111/inm.12596] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30953420]
75. Fortuna, K.L.; Aschbrenner, K.A.; Lohman, M.C.; Brooks, J.; Salzer, M.; Walker, R.; George, L.S.; Bartels, S.J. Smartphone ownership, use, and willingness to use smartphones to provide peer-delivered services: Results from a national online survey. Psychiatr. Q.; 2018; 89, pp. 947-956. [DOI: https://dx.doi.org/10.1007/s11126-018-9592-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30056476]
76. Kaufman, T.K.; Lynch, B.A.; Wilkinson, J.M. Childhood obesity: An evidence-based approach to family-centered advice and support. J. Prim. Care Community Health; 2020; 11, 2150132720926279. [DOI: https://dx.doi.org/10.1177/2150132720926279] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32501125]
77. Heerman, W.J.; Schludnt, D.; Harris, D.; Teeters, L.; Apple, R.; Barkin, S.L. Scale-out of a community-based behavioral intervention for childhood obesity: Pilot implementation evaluation. BMC Public Health; 2018; 18, 498. [DOI: https://dx.doi.org/10.1186/s12889-018-5403-z]
78. Swindle, T.; McBride, N.M.; Selig, J.P.; Johnson, S.L.; Whiteside-Mansell, L.; Martin, J.; Staley, A.; Curran, G.M. Stakeholder selected strategies for obesity prevention in childcare: Results from a small-scale cluster randomized hybrid type III trial. Implement. Sci.; 2021; 16, 48. [DOI: https://dx.doi.org/10.1186/s13012-021-01119-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33933130]
79. Barkin, S.L.; Gesell, S.B.; Po’e, E.K.; Escarfuller, J.; Tempesti, T. Culturally tailored, family-centered, behavioral obesity intervention for Latino-American preschool-aged children. Pediatrics; 2012; 130, pp. 445-456. [DOI: https://dx.doi.org/10.1542/peds.2011-3762] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22869834]
80. World Health Organization. About WHO’s Work on Health Inequality Monitoring. Available online: https://www.who.int/data/inequality-monitor/about (accessed on 30 May 2024).
81. Al-Mondhiry, J.; D’Ambruoso, S.; Pietras, C.; Strouse, T.; Benzeevi, D.; Arevian, A.C.; Wells, K.B. Co-created mobile apps for palliative care using community-partnered participatory research: Development and usability study. JMIR Form. Res.; 2022; 6, e33849. [DOI: https://dx.doi.org/10.2196/33849]
82. Hu, J.; Perer, A.; Wang, F. Data driven analytics for personalized healthcare. Healthcare Information Management Systems: Cases, Strategies, and Solutions; Springer: Cham, Switzerland, 2016; pp. 529-554.
83. Torous, J.; Firth, J.; Huckvale, K.; Larsen, M.E.; Cosco, T.D.; Carney, R.; Chan, S.; Pratap, A.; Yellowlees, P.; Wykes, T. et al. The emerging imperative for a consensus approach toward the rating and clinical recommendation of mental health apps. J. Nerv. Ment. Dis.; 2018; 206, pp. 662-666. [DOI: https://dx.doi.org/10.1097/NMD.0000000000000864]
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Li, Yan
; Montayre, Jed
; Li, Mengqi; Ka Yan Ho
; Li, Jiaying; Yorke, Janelle