As the first step, the study systematically explores and synthesizes the existing literature on LLs within digital health contexts. The primary objective is to analyze their nature and roles while identifying critical knowledge gaps to guide future research and practice. To achieve this, the study is structured around three core sub-questions. RQ 1.1 How are LLs defined and conceptualized within digital health settings? This inquiry examines their distinctive features, particularly their integration into real-life environments and their contributions to fostering health-related innovations. The second question investigates RQ 1.2: What unique practices characterize stakeholder involvement in LLs? It focuses on how LLs differ from other innovation methodologies, emphasizing their capacity to engage diverse stakeholders and address broader determinants of health. The third question addresses RQ 1.3: What are the key outcomes and impacts of LL in digital health, and what challenges and barriers affect their effectiveness? It considers issues such as the integration of LLs' services, the sustainability of their initiatives, and the scalability of their innovations. A systematic scoping review was chosen for its ability to provide a structured and rigorous synthesis of a broad and diverse body of literature (20). Unlike traditional scoping reviews (18), this approach ensures a transparent and replicable process, essential for an emerging and interdisciplinary field like digital health, where LLs often involve predominantly qualitative or descriptive studies (20). The search strategy was developed in accordance with established systematic review guidelines to ensure both comprehensiveness and precision. Adherence to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ensures a rigorous and transparent literature review (21). This thorough approach enhances the reliability of the findings and supports the validity of the study conclusions (see Figure 1).

The review began with a comprehensive search of relevant literature across multiple electronic databases, including Web of Science, Scopus, Google Scholar, PubMed, Embase, CINAHL, and PsycINFO. Given the diverse terminology used by scholars and professionals to describe the living lab phenomenon, the search included multiple keywords, such as “living lab^*”, “living laboratory”, “living laboratories”, and “living labbing”. For health categories, the search incorporated terms like “health”, “healthcare”, and “care”. Regarding digital health, the query included keywords such as “electronic health”, “telemedicine”, “telecare”, “telehealth”, “mobile health”, “mHealth”, “e-health”, “digital medicine”, and “home care”. This query was explicitly designed to capture a wide range of studies focusing on stakeholder engagement within the context of digital health innovations (22). The initial search, conducted in September 2023 and updated in February 2024, identified 1,102 documents published up to December 2023.

Following a preliminary screening of titles, keywords, and abstracts, 541 documents were excluded due to duplication, lack of relevance to LLs, or health. This step reduced the pool to 914 studies. Selection proceeded in two phases. The first phase screened publishers, titles, and abstracts, prioritizing peer-reviewed empirical studies in English while excluding non-empirical research, such as reviews and editorials, to ensure a focus on concrete data and outcomes. Two independent authors conducted this screening, resolving discrepancies with a third reviewer, and ultimately excluded 733 articles, leaving 181 for further analysis.

In the second phase, a quality assessment of included studies was conducted using the Mixed Methods Appraisal Tool (MMAT) 2018, a validated instrument for evaluating qualitative, quantitative, and mixed-methods research. Its selection followed Archibald's (20) protocol, ensuring methodological rigor and inclusivity. The MMAT assesses three core criteria, rated as “yes”, “no”, or “cannot tell”, allowing for the inclusion of studies with well-defined research questions without imposing excessively stringent thresholds. This approach ensured a broad representation of LLs‘ activities and findings. Three authors independently assessed the documents to ensure consistency and reliability, with discrepancies resolved through collective discussion. This process led to the inclusion of 56 studies in the qualitative synthesis, offering a broad representation of LLs' activities and findings. Building on this foundation, the data extraction phase was designed to capture and organize the complexity of the selected studies in a coherent and structured manner. To address the substantial heterogeneity in study designs, contexts, and focus areas, the same three authors conducted the extraction independently, drawing on a predefined grid based on shared analytical dimensions. These included research objectives, methodological approaches, intervention settings, stakeholder roles, technological applications (e.g., IoT and sensors), outcomes, and challenges. This structured process supported both the subsequent thematic analysis and narrative synthesis, providing a robust foundation for identifying cross-cutting patterns and emerging themes.

The thematic analysis followed the structured approach outlined by Thomas and Harden (23), which involves systematic coding and synthesis of qualitative data. The coding process was iterative, allowing for refinement and adjustment as new insights emerged. For example, text segments describing stakeholder roles in LLs and activities were initially coded under specific categories, such as “stakeholder participation”, which were later grouped into the broader theme- stakeholder engagement and practices – representing the main topic of interest across the different studies, ensuring that the synthesis captured the richness of the data. Similarly, the analysis proceeded with other themes to ensure that they were directly aligned with our research sub-questions: how LLs were defined and conceptualized within health settings, the distinctive features of stakeholders' engagements, the primary outcomes and the challenges and barriers they face.

To contextualize the findings, the study adopted the narrative synthesis approach proposed by Popay et al. (24). This method went beyond identifying themes by integrating them into a cohesive narrative that detailed their interactions and implications within the broader framework of digital health innovation. For instance, stakeholders and their roles were analyzed within the narrative synthesis to explore how these elements influenced their roles. This approach highlighted the dynamic relationships between themes, offering a new framework for understanding the intricacies of LLs and their crucial role in integrating digital interventions within the evolving landscape of broader health goals. The six foundational principles of the European Network of Living Labs (ENoLL) (9) were used as an initial guide to systematically construct a conceptual structure defining the nature of Living Labs.

This preliminary framework was further explored and refined through two focus group sessions with experts in digital health, which supported the development of a context-sensitive conceptual model of Living Labs oriented toward health service integration. Each session lasted approximately 90 minutes and followed established protocols for qualitative focus group research (19), ensuring methodological consistency and depth. The groups were composed of seven participants representing a range of roles and experiences in digital health innovation: two clinical physicians (one with expertise in geriatrics, one in primary care), one biomedical engineer, one digital health researcher, one manager from a regional health authority, and two informal caregivers (one supporting an older adults parent, the other a chronically ill partner). Participants varied in age (35–68 years) and gender (four female, three male), contributing diverse perspectives across professional and personal domains. A semi-structured interview guide was used to ensure consistency across sessions while allowing for emergent insights. The guide covered topics such as stakeholder engagement strategies, integration of LLs into existing health systems, evaluation of health outcomes, and perceived barriers and enablers to scaling LL-based interventions. The discussions were audio-recorded and transcribed verbatim. The focus of the interpretation was to deepen and consolidate the thematic areas identified in the literature review, clarify how they relate to one another, and integrate participants' perspectives into a coherent synthesis. This process highlighted both convergence and divergence in viewpoints, enriching the development of the proposed framework.

The findings from the systematic literature review reveal a detailed classification of the analyzed studies based on their primary focus: whether they concentrate directly on the concept of LLs or employ LLs as a methodological approach. Of the 56 articles reviewed, 27 specifically examined the idea of LLs, highlighting their effectiveness in involving end-users in the co-creation process—the remaining 29 articles employed LLs as a methodological framework for their research. The analysis also explored the distinguishing features of LLs, examining how they are defined and conceptualized in health, their fields of application within digital health, the innovations implemented and geographical distribution.

Regarding definitions (see Supplementary Table 1), LLs are most characterized as open innovation ecosystems (25, 26), a perspective that emphasizes their ability to foster collaborative innovation and experimentation among multiple stakeholders, including researchers, industry actors, and end-users. A common interpretation also frames LLs as innovation networks, highlighting these ecosystems as interconnected and dynamic processes (27, 28). A significant body of literature refers to the European Network of LLs (ENoLL) definition, highlighting its user-centred approach, which emphasizes the integration of user needs and feedback throughout the innovation lifecycle (29–31). Additionally, some studies mention the LLs environment as a shared collaboration space designed to replicate users' real-life contexts, including hospitals, health facilities, and private home settings (32). Some of these works also explore this concept within virtual or digital environments, further expanding its applicability and reach (28, 33).

LLs are increasingly prevalent in primary care, as evidenced by numerous studies (33, 34) (see Supplementary Table 2). These LLs often focus on addressing various chronic diseases, a significant area of concern in health. Research highlights their application in managing diabetes, cardiovascular diseases, and other long-term health challenges (35–37). In addition, studies focus on the role of LLs in preventive health, including fall risk prevention and alcohol misuse prevention (38, 39), as well as promoting psychological well-being and addressing mental health disorders, such as depression and anxiety, alongside broader psychological and social interventions (40, 41). Furthermore, emergency medical situations, including acute care scenarios, have also been explored within the LLs framework, emphasizing their adaptability to critical health challenges (27, 42). Beyond these specific applications, some studies explore LLs without anchoring them to a particular medical context. These works focus on integrating smart technologies, overarching principles, and guidelines for implementing LLs in diverse health settings. They highlight the potential of LLs as a testbed for innovative solutions, emphasizing technological advancements and collaborative approaches (43, 44).

The health-related innovations emerging from LLs primarily focus on developing new technological solutions (see Supplementary Table 3). These innovations include mobile applications (25, 45) and exergames and gamified exercises (31, 46), software platforms (47, 48), wearable devices and sensors (49–51), as well as robotics and chatbots (28, 52, 53). Additionally, several studies propose using LLs to develop novel methodologies, protocols, and procedures. These methodologies encompass diverse applications, such as enhancing medication adherence through tailored interventions (26), facilitating recruitment, surveys, and early-stage testing via collaborations between citizens and institutions or companies (54), and implementing integrated health models, such as the Epital Care Model (ECM) for remote monitoring and self-management of chronic diseases (55).

Most of the analyzed publications focus on the operations of LLs based in Europe (56, 57). These studies document LLs experiences in countries such as the Netherlands (26, 58), Germany (30, 46, 59), France (49, 60), and other Central European nations. Additionally, a smaller subset of articles explores LLs initiatives in Asia, specifically in South Korea (53, 61) and the Americas, primarily in the United States and Canada (42, 51).

The literature analysis reveals a diverse array of theoretical frameworks and methodologies underpinning the design and implementation of LLs. Among frameworks are User-Centered Design (UCD) and participatory approaches, which emphasize co-creation and collaborative design processes (31, 45, 62). Open Innovation and Technology Adoption, focusing on frameworks such as UTAUT and Technology Acceptance Models, are also identified to explore factors influencing user acceptance and the adoption of LLs' outputs (46, 63). A prominent theme emerging from the analysis is the integration of the Quadruple and Quintuple Helix frameworks, which expands the discourse to encompass ecological sustainability (44, 48, 57) and aligns with the principles of the circular economy (58). In some studies, this integration further emphasizes sustainable and ethical engagement alongside the promotion of health equity through various initiatives (4, 14, 43), including those led by the World Health Organization. Sociocultural and psychological frameworks also contribute to the depth of user experience design in LLs, drawing on perspectives from social-cognitive theory, cultural psychology, and wellness paradigms (33, 53, 64). Finally, advanced technological frameworks play a pivotal role in the multidisciplinary approach of LLs. These frameworks incorporate systems thinking (65) alongside cutting-edge technologies, such as the Internet of Things (IoT), edge computing, and behavioral sensing, to improve health outcomes and quality of life (47, 66, 67).

Qualitative methodologies, including grounded theory, thematic and phenomenological analysis, participatory research, and ethnographic methods, dominate studies that explore user experiences and ethical considerations, reflecting the centrality of human-centred inquiry in LLs contexts (37, 41, 58). Quantitative approaches, including surveys, physiological assessments, and comparative studies, provided structured data to measure usability, health outcomes, and the effectiveness of interventions (30, 38, 61, 67). In addition, experimental designs, such as randomized controlled trials and pilot studies, underscore the commitment to rigorously testing interventions in real-world settings (33, 42, 55, 59). Mixed methods integrate user feedback with quantitative assessments, enhancing the robustness of iterative co-creation processes (4, 45, 52).

The analysis of stakeholder roles and collaborative activities in LLs underscores the complexity and inclusivity of these ecosystems, highlighting the diverse contributions of various actors.

Patients are central to these ecosystems, whose lived experiences and contextual knowledge form the foundation for user-centred and inclusive solutions (see Supplementary Table 4). Beyond patients, other key stakeholders are identified as primary users, including general users (46, 68), caregivers (41, 69), and vulnerable groups (29, 44). Among professionals, the roles of healthcare practitioners (34, 59, 70), designers and developers (38, 58), and researchers (4, 71) are particularly emphasized. Other relevant categories include healthcare organizations, such as healthcare facilities (43, 49), educational institutions (32, 72), and research centers (57, 73). Some studies explicitly reference policymakers (44, 55), governments and regulatory authorities (54, 73), communities (42, 66, 74), and citizens (34).

These stakeholders participate in various co-creation activities, which can be identified in four main stages: co-design, co-development, co-testing, and co-evaluation. During these stages, collaboration becomes critical to the success of LLs initiatives (see Supplementary Table 5).

Co-design practices employ inclusive methodologies that enable stakeholders to ideate and frame innovative solutions collaboratively. Participatory workshops emerge as a central tool for fostering joint ideation, providing a structured yet open environment for brainstorming and insights generation. For instance, workshops have been instrumental in creating strategies for promoting healthy behaviors (65) and tailoring mobile applications for patients (46). Contributions from marginalized groups facilitated through discussions or stakeholder mapping (40) enrich these processes, ensuring that solutions address diverse societal challenges (74). In parallel, healthcare professionals, designers and usability experts collaborate through workshops to ensure accessibility, leveraging iterative feedback to refine ideas into actionable insights (26). Policymakers also contributed to these activities by addressing structural challenges (44) or designing frameworks and policies to ensure long-term impacts (73).

Co-development practices prioritize participatory approaches that bring stakeholders together to prototype and refine solutions. Iterative design cycles, including scenario-based methodologies and participatory service design, are widely applied to integrate user preferences and technical requirements (45, 48). For instance, researchers and clinicians have collaboratively developed emergency response systems for smart homes, ensuring that the technologies meet functional and clinical standards (42). Similarly, the co-development of companion robots involved interdisciplinary teams, including technical experts, who integrated cognitive and ergonomic considerations to enhance usability (53). Healthcare providers further supported these processes by aligning innovations with resource constraints and scalability objectives (68).

Co-testing practices focus on validating solutions through real-world experimentation and simulated scenarios, ensuring their functionality and usability. End users, particularly patients, actively participate in trials of wearable devices, providing critical feedback on reliability and usability while biomedical engineers assess technical accuracy (51). Behavioral observations, often facilitated by sociologists and anthropologists, complement these evaluations by providing contextual insights into user interactions (41). Simulations, such as VR interventions, exemplify how stakeholder collaboration can refine developmental tools for targeted populations (39). Small and medium-sized enterprises (SMEs) and technology providers play a pivotal role in bridging innovation with market readiness, ensuring solutions are tailored to diverse operational contexts (63).

Evaluation practices involve systematic assessments to optimize LLs solutions based on user feedback and performance metrics. The literature emphasizes the role of iterative evaluations in refining eHealth tools, apps, and assistive devices (45, 65). For instance, older adult participants evaluated the usability of assistive technologies, contributing to iterative improvements (49), while caregivers and healthcare practitioners identified barriers to implementation and proposed enhancements. Risk assessments, facilitated by partner organizations and institutional stakeholders, ensure solutions adhere to safety and usability standards, promoting inclusivity and scalability across diverse settings (64). Performance data, such as heart rate variability metrics, further validate device reliability and guide refinements to mobile health applications (51).

While LLs are designed to foster the development of innovative solutions, reported outcomes vary considerably in scope and impact, spanning different stages of the innovation process (see Supplementary Table 6). Many studies focus on the development phases (27, 33, 41, 57), while others focus on the implementation phase (31, 44, 75). Few studies investigate the application of LLs components in user practices (26, 48).

Concerning outcomes, the versatility of Living Labs as an open innovation framework lies in their capacity to support the advancement of health research, facilitate service development, and drive health transformation. A key contribution of Living Labs lies in their role in advancing health research methodologies. Several studies emphasize their impact on refining co-creation techniques and iterative design, ensuring that health interventions align with user needs and practical applications (4, 41, 75). Living Labs facilitate the collection of rich, real-world data, combining qualitative insights from user interactions with quantitative health metrics (33, 72), thereby ensuring the feasibility and impact of interventions. Moreover, interdisciplinary collaboration fosters innovation by engaging researchers, practitioners, and patients in co-developing solutions tailored to complex healthcare challenges (31, 45). Beyond their research role, Living Labs play a pivotal role in driving healthcare innovation. These environments enhance service quality and patient outcomes by facilitating optimized health coordination (76), integrating advanced technologies, and streamlining healthcare processes (39, 51). Moreover, Living Labs actively foster patient engagement (33, 37) and empower individuals to take an active role in their healthcare, ultimately improving health outcomes and compliance (25, 55).

Additionally, they drive e-health innovation by incorporating digital health solutions and data-driven strategies, thereby advancing ageing-in-place initiatives and preventive care (35, 44). Beyond these functions, Living Labs drives systemic health transformation, shaping resilient and inclusive healthcare systems. They enhance access to healthcare, particularly for underserved populations (37, 65), while fostering social participation and empowering marginalized communities through participatory engagement (31, 60). Moreover, they promote health literacy and develop educational programs and training initiatives to facilitate the adoption of digital health (4, 41). Furthermore, some studies have demonstrated their role in supporting policy development and business growth, influencing healthcare policies, clinical guidelines, and service delivery models through real-world evidence (40, 43).

Across various studies, multiple implementation outcome measures have been identified, although emphasis on specific aspects varies (see Supplementary Table 8). Most studies emphasize the acceptability (30, 77) and appropriateness (31, 44) of LL methods and approaches. Additionally, several studies report positive results regarding the effectiveness and efficiency of innovative solutions (38, 51). Other studies highlight end-user satisfaction (26, 78) and appreciation for the functionality of LLs (37, 79). Furthermore, aspects such as safety (71) and equity (32, 48, 77) have also been investigated.

Despite their transformative potential, LLs face several persistent challenges (see Supplementary Table 9). Ensuring digital health solutions are accessible and user-friendly remains a significant challenge, particularly for older users (46, 58) and vulnerable populations (26, 45). Furthermore, stakeholder engagement is often complicated by conflicting interests, unclear roles, and difficulties fostering effective collaboration among multidisciplinary teams (48, 63). Securing long-term funding and ensuring financial sustainability also pose significant hurdles, as many LLs struggle to transition from pilot phases to fully operational healthcare solutions (29, 56). Additionally, concerns regarding privacy, informed consent, and ethical guidelines continue to be a pressing issue, necessitating more explicit frameworks for responsible data use and participant protection (31, 69). Similarly, the scaling and integration of living lab outcomes into broader healthcare systems remains complex, requiring robust policy support and regulatory alignment (35, 44).

In response to these challenges, the analyzed studies identify as research priorities (see Supplementary Table 10) the integration of longitudinal studies to assess long-term effectiveness and scalability (42, 68) and the development of scaling methodologies to ensure successful innovations extend beyond pilot projects into broader healthcare systems (58, 77). Economic feasibility and cost-effectiveness remain critical areas of inquiry, essential for evaluating the financial sustainability of living labs (56, 64). Advancing AI-driven healthcare solutions is also a priority, particularly in enhancing predictive analytics, patient monitoring, and decision-making (29, 52, 78). However, these innovations must align with inclusive digital health strategies to promote health literacy and digital equity, particularly among marginalized populations (25, 51). Finally, policy development must keep pace with technological advancements, ensuring that regulatory frameworks facilitate, rather than hinder, the integration of living labs into healthcare systems (32, 43).

The CLEA framework is articulated around three core elements: (1) a renewed conceptualization of Living Labs, (2) a structured, stakeholder-driven model of collaborative practices, and (3) the integration of LLs within healthcare ecosystems to generate measurable health and societal impact.

First, LLs in digital health are defined as the following:

LLs are open innovation ecosystems conceived as user-centred networks. In these ecosystems, multiple stakeholders—patients, citizens, caregivers, researchers, healthcare professionals, policymakers, and businesses—collaborate to innovate, develop, and implement new health interventions that encompass innovative health services, therapeutic solutions, and technologies while identifying emerging research opportunities.

This definition expands on previous works emphasizing the user-centred and co-creation approach (8, 16) by incorporating multidimensional aspects of health. In line with Kim et al. (17), it underscores the necessity for the systemic integration of research and practice to meet evolving healthcare demands. These platforms integrate technologies, practices, and methods to address citizen-centred health and societal needs. Thus, the definition of the CLEA intervention is completed by specifying that:

LLs interventions integrate technologies, practices, and methods that can be implemented in physical (e.g., healthcare settings, homes, etc.) and virtual environments. LLs simulate or replicate the real-life contexts of users (patients, citizens and communities) within multidisciplinary research areas and settings.

Second, the CLEA framework offers actionable insights to enhance Living Lab interventions by emphasizing a stakeholder-driven approach. Innovation within LLs often tackles complex, multifaceted challenges, necessitating input from diverse perspectives (45). Unlike previous studies broadly addressing stakeholder participation, the CLEA framework uniquely details these participations. The proposed framework comprehensively classifies stakeholder roles within the LLs ecosystems. Specifically, Table 2 categorizes stakeholders into five distinct groups based on their contributions to key collaborative activities, including co-creation, co-design, co-development, co-testing, and co-evaluation. The insight contributors (1), mainly patients, citizens, and vulnerable groups, inform design through lived experiences; the knowledge specialists (2), including healthcare professionals and researchers, drive co-design, development, and evaluation with technical and clinical insights; the developer strategists (3), such as healthcare providers, developers and researchers, according to their specific competences, refine and test the interventions in real-world contexts, the innovation enablers (4), including startups and corporate partners, integrate innovations into systems while ensuring scalability and market readiness, and the facilitators (5), such as policymakers and communities, promote ethical alignment and societal acceptance through participatory frameworks. By explicitly identifying and differentiating stakeholder roles based on specific activities—such as co-design or co-development, this framework moves beyond simplistic stakeholder classification to address their practical contributions. It also recognizes that a single stakeholder may transition between roles at different stages of the living lab experience (13). By adopting this dynamic perspective, the proposed framework effectively bridges the gap between theoretical stakeholder engagement and practical implementation in LLs. This approach provides actionable insights for mobilizing diverse expertise throughout the innovation LLs lifecycle.

Third, CLEA extends beyond merely a method or a collaborative innovation context; instead, it positions the LLs primarily as an integrative healthcare ecosystem designed to connect and optimize various functionalities. Numerous studies emphasize the diverse outcomes associated with the Living Lab approach, including enhanced healthcare accessibility, improved efficiency, and increased innovation, while also addressing key priorities such as personalized services, training support, and cost reduction (13, 17). To provide a structured analysis of these multisided outcomes, this study categorizes them into three primary domains: methodological advancements, innovation-driven applications, and systemic impact. Methodological advancements highlight the role of Living Labs in enhancing research rigor, facilitating co-creation, and ensuring that healthcare research is both scientifically robust and practically applicable (4, 16). By integrating participatory design, real-world testing, and interdisciplinary collaboration, Living Labs bridges the gap between controlled research environments and real-world healthcare challenges, thereby strengthening the evidence base and practical relevance of health interventions. Innovation-driven applications leverage cutting-edge technologies, patient-centred digital tools, and data-driven approaches to advance healthcare service provision, enhance patient self-management, and support informed decision-making (17). By optimizing service provision and fostering the integration of preventive and ageing-in-place strategies, Living Labs contribute to the development of more effective and personalized healthcare solutions. The systemic impact extends the role of LLs beyond individual interventions, addressing broader structural challenges it encourages by expanding healthcare access, promoting social inclusion, and driving policy and educational growth (12, 43, 58). Through their role in informing regulations and ethical frameworks, supporting workforce training, and fostering sustainable innovation ecosystems, Living Labs contribute to long-term healthcare transformation and equitable service delivery. A concrete example is provided by a recent immersive virtual reality (VR) study for managing dental phobia (81). Clinicians, engineers, and researchers co-designed a VR system tailored to dental procedures, aligning interaction constraints with clinical ergonomics. The solution, tested in a university hospital on odontophobic patients, integrated symbolic interaction to preserve patient immobility during treatment. Real-time testing, combined with physiological monitoring and feedback from both patients and clinicians, enabled iterative refinement (82).

To further structure this categorization, this study builds on the assessment framework proposed by Proctor et al. (81, 82), which offers analytical tools for evaluating the effectiveness, feasibility, and impact of research implementation initiatives across various domains. While previous studies have explored the implementation of Living Lab innovations through Proctor's framework to varying degrees, only a few have considered the full spectrum of potential outcomes (11). Expanding on this approach, the present study introduces overarching indicators that establish a direct connection between implementation outcomes and specific Living Lab outcome domains. This integration provides a more structured and systematic evaluation tool for assessing the impact and effectiveness of Living Lab models. As illustrated in Table 3, the study aligns Proctor's indicators with a categorization of Living Lab outcomes and further proposes concrete, implementable measures tailored to each identified area of interest.

By consolidating these dimensions, CLEA offers a comprehensive architecture for understanding and operationalizing Living Labs in healthcare. Its added value becomes clear when situated within the broader landscape of existing Living Lab initiatives in Europe. While the framework draws conceptual inspiration from the ENoLL, it advances beyond current implementations by offering a more formalized and evaluative structure for health-innovation ecosystems. For example certified ENoLL Living Labs such as the Healthcare Living Lab Catalonia (83) demonstrate the value of co-creation, prototyping, and real-life validation of digital health technologies or initiatives like the Galician Network of Health Living Labs promote collaborative healthcare solutions across institutional settings (84). However, although these models successfully embody user-centred innovation in real-life contexts, they often lack formalized mechanisms for classifying stakeholder roles, integrating services across the continuum of care, and systematically evaluating implementation outcomes. CLEA addresses these limitations through a structured conceptual framework that promotes integrated service innovation aligned with health system needs and systemic collaboration, defines dynamic stakeholder roles, and links implementation strategies to measurable systemic impacts.