Cardiovascular disease (CVD) remains the leading cause of mortality globally, accounting for an estimated 20.5 million deaths in 2021, a 60% increase since 1990 [1]. The burden is driven by a complex interplay of modifiable behavioral risk factors (eg, low diet quality, physical inactivity, alcohol, and tobacco use), biological risk factors (eg, hypertension, dyslipidemia, impaired glucose regulation), and psychosocial risk factors (eg, stress, depression, and social isolation) [2]. Despite the availability of cost-effective prevention, detection, and treatment strategies, national responses have been insufficient due to policy inertia, systemic fragmentation, and the negative influence of detrimental social and commercial determinants of health [3].

Addressing this knowledge-action gap requires place-based, systems-oriented initiatives that mobilize local assets, engage multisector stakeholders, and embed adaptive evaluation in real-world contexts [4,5]. Such initiatives can leverage systems science to identify and respond to dynamic changes over time [5], generate locally relevant evidence to guide action, and contribute to broader policy and practice [5]. However, few existing models have operationalized comprehensive, co-designed data frameworks to sustain coordinated action for cardiovascular health.

Greater Springfield, a rapidly growing master-planned city in South-East Queensland, Australia, provides a unique setting for such an initiative. Its defined geographic boundaries, integrated urban infrastructure, and strong local governance align to suggest its relevance as a “living lab,” a place where multistakeholder collaboration, embedded research, and iterative development can address complex health challenges. The Springfield Healthy Hearts initiative positions Greater Springfield as a demonstration city for heart health through coordinated, systems-based action. Our previous work described the conceptual foundations of Springfield Healthy Hearts, including its systems ontology, transdisciplinary epistemology, and methodological principles.

This paper will build upon the Springfield Healthy Hearts concept by outlining the Springfield Healthy Hearts Data Framework, a multicomponent system that will be implemented to guide, monitor, and evaluate coordinated action for heart health in Springfield. By specifying how we will translate the Springfield Healthy Hearts vision through the operation of the data framework, this protocol will provide a replicable model for other communities to follow.

Springfield Healthy Hearts is grounded in a systems approach [6] that recognizes cardiovascular health as an emergent property (characteristic) of interconnected social, environmental, economic, and health care factors (Figure 1). The initiative’s ontology acknowledges that these systems are dynamic, adaptive, and shaped by interactions, ranging from individual behaviors to policy environments. The Springfield Healthy Hearts Data Framework operationalizes this approach using 3 interlinked components, followed by a fourth component that integrates and synthesizes the generated data. These components are designed to operate as an integrated system, with data flowing between levels to inform coordinated action.

Coordinated action projects will be quasiexperimental in design, with outcomes captured through the project evaluation component of the framework. Community-level risk for CVD will be assessed using a repeated cross-sectional evaluation through the community evaluation component of the framework. The city evaluation component will consist of a descriptive ecological design, collecting aggregate indicators known to affect heart health.

The data framework was developed through a structured, co-design process (University of Queensland Human Research Ethics Committee 2024/HE002099) involving community members, local health professionals, researchers, and local delivery partners and health services. The co-design process comprised iterative discussions with diverse informants to explore, refine, and prioritize research questions, data collection procedures, and outcome measures, ensuring local relevance and acceptability [7].

The data framework co-design process was approved by the University of Queensland Human Research Ethics Committee (2024/HE002099). Informed consent was obtained from participants prior to participation, and each participant received appropriate remuneration for their involvement as per Health Consumers Queensland guidelines [8]. Given the integration of multiple data sources, there is a potential risk of reidentification. This will be mitigated through deidentification procedures and secure data linkage protocols. Data will be analyzed and reported in aggregate form to protect participant confidentiality. AI analyses will be conducted on deidentified data and will not be used for individual-level decision-making. Future components mentioned in our protocol (eg, the coordination action projects) will undergo the appropriate research ethics committee review and approval process.

Adequate compensation (cash vouchers, approximately Aus $30 to $50 [Aus 1=US $0.69 as of July 8, 2026] per completed assessment component) for participants is essential to ensure participation and completion of the study and will be provided to participants after they complete each study component, in line with institutional ethical approval and intended to support participation without undue inducement. Participants will be given the option to “donate” their incentives back to the study if they wish. For participants less than 18 years of age, incentives will be provided to their parent/guardian.

These data streams will feed into a live analytics environment that iteratively develops and tests theories of impact, explaining how coordinated action projects and environmental change influence heart health. Data synthesis will involve the integration of project-, community-, and city-level data using both traditional statistical approaches and exploratory methods. Multilevel modeling and pattern recognition techniques may be used to identify relationships across data sources. The handling of missing data will be tailored to the type and source of data within each component of the framework. Analyses will combine AI-enabled pattern recognition with structured hypothesis testing and safeguards, grounded in current evidence on CVD pathophysiology. These may include supervised and unsupervised machine-learning techniques, such as clustering and predictive modeling. These methods will be used to support the identification of relationships between coordinated action, environmental factors, and cardiovascular risk, particularly where traditional modeling approaches may be limited by complexity or dimensionality. Findings will be interpreted in collaboration with domain experts to ensure validity and relevance for decision-making. To minimize potential bias, model performance will be examined across key demographic subgroups, and input variables will be assessed for sources of structural bias. AI methods will be used to complement, rather than replace, hypothesis-driven analyses within the framework. Findings will be visualized through dynamic dashboards that provide real-time feedback, guiding decisions about priority coordinated action projects. Dashboard outputs will also support transparent reporting to partners, funders, policymakers, and the community. By embedding both traditional and emerging AI-enabled analytics within a tested systems architecture, the Springfield Healthy Hearts Data Framework will operate as a continuous learning and decision-support system, able to adapt, optimize, and scale coordinated action projects on heart health.

The Springfield Healthy Hearts Data Framework is designed as an integrated, multilevel system rather than a set of discrete components. The 4 components operate together to support continuous learning and adaptive implementation. Coordinated action projects (component 1) generate data on implementation, reach, and effectiveness at the intervention level. Where participant consent is provided, these data may be linked to individual-level data collected through the community evaluation (component 2), enabling the examination of exposure-response relationships and individual risk profiles. City evaluation data (component 3) provides contextual information on environmental, social, and service-level factors, including health service utilization, built environment characteristics, and population-level trends. These data allow for the interpretation of intervention outcomes within the broader system context. Data synthesis (component 4) integrates these multilevel datasets through iterative analytical processes. Findings are translated into actionable insights and shared with implementation partners and the Springfield Healthy Hearts Alliance. These insights inform the refinement of existing coordinated action projects and prioritization of new initiatives. This process operates as a continuous feedback loop, in which data inform action and action generates new data. Over time, this enables adaptive system improvement and supports the development of scalable, evidence-informed approaches to CVD prevention.

All components of the data framework will be synthesized through a secure, continuously updated data management platform built on a secure Trusted Research Environment (TRE) infrastructure. The TRE ensures compliance with institutional, national, and international standards for data security and integrity, including encryption, role-based access, and audit trails. Data will be stored for a minimum of 15 years in line with the university’s research data management policy.

External researchers may apply for access to data through a formal application process. Applications will be reviewed by the Springfield Healthy Hearts Data Committee, which will assess proposals based on scientific merit, alignment with project objectives, ethical considerations, and data minimization principles. Approved researchers will access data within the TRE, where data remain deidentified and cannot be downloaded or removed. Access will be role-based and time-limited, with audit logs maintained to monitor data use. All external collaborations will require appropriate data sharing agreements and ethics approvals where applicable.

The Springfield Healthy Hearts Alliance will meet quarterly and serve as the overarching governance body, bringing together research, community, and partner perspectives. The Alliance will be supported by the following three committees:

The committees will meet quarterly and report to the Alliance, ensuring transparency, accountability, and alignment of data use with the strategic and ethical objectives of Springfield Healthy Hearts. This governance structure reflects our commitment to embedding community voice and translational pathways at the heart of the project.

Access to raw data will be restricted to the core Springfield Healthy Hearts research team, with permissions tailored according to roles and project responsibilities. Curated datasets may be accessed for approved analyses following a review by the project’s Data Committee. The Data Committee will oversee data governance, including the management of access requests, feasibility, and management of data linkages. External researchers may apply for data access through the committee, subject to data use agreements.

This protocol describes a novel, multilevel data framework designed to support coordinated, adaptive approaches to CVD prevention at a city scale. By integrating data from coordinated action projects, community-level assessments, and city-level monitoring, the framework is expected to generate new insights into how behavioral, biological, and contextual factors interact in real-world settings. Importantly, this approach enables continuous learning, allowing interventions to be refined over time and supporting the development of scalable, evidence-informed strategies for improving cardiovascular health. This represents a shift from evaluating individual programs to understanding how prevention can be delivered effectively across an entire community.

The Springfield Healthy Hearts Data Framework represents a novel, operationalized example of a place-based, co-designed, multilevel data system for cardiovascular health. Unlike traditional program evaluations or isolated surveillance activities, this framework integrates project-, community-, and city-level data within a continuous learning system, enabling real-time adaptation of coordinated action. By embedding systems thinking into design and implementation, Springfield Healthy Hearts aligns with global calls for interventions that recognize health as an emergent property of complex adaptive systems [40,41]. The co-design process ensures that the framework is grounded in local priorities and assets, enhancing its legitimacy and sustainability [7,42]. The integration of administrative, self-reported, and environmental data maximizes coverage and reduces blind spots in both population and system-level surveillance [43].

Several limitations should be acknowledged. First, some measures rely on self-reported data, which may be subject to recall or social desirability bias. Second, while data linkage across components offers analytical advantages, it introduces practical and methodological challenges, including ensuring data compatibility and maintaining data privacy. Third, the interpretation of city-level data carries a risk of ecological fallacy, whereby associations observed at the population level may not reflect individual-level relationships. These limitations are inherent to complex, real-world research and will be considered in the interpretation of findings.

Implementing a multilevel data framework in a real-world setting presents several challenges. These include coordinating across multiple sectors and organizations, maintaining consistent data quality across diverse data sources, and sustaining participant and partner engagement over time. To mitigate these challenges, the Springfield Healthy Hearts Living Lab adopts a collaborative governance model, with ongoing engagement of partners through the Springfield Healthy Hearts Alliance. Standardized protocols and training procedures are used to support data quality, while flexible implementation approaches allow adaptation to local contexts. Sustained engagement will be supported through regular communication, feedback of findings to partners and the community, and alignment of data activities with existing services and priorities. These strategies aim to enhance the feasibility, acceptability, and long-term sustainability of the data framework.

The Springfield Healthy Hearts Data Framework has implications for both research and practice in CVD prevention. For research, this framework demonstrates how multilevel data can be integrated to examine the dynamic relationships between individual risk factors, environmental conditions, and coordinated interventions in real-world settings. It provides a foundation for moving beyond the evaluation of isolated programs toward understanding how prevention operates as a system. For practice, the framework offers a structured approach for translating data into coordinated action across sectors, enabling health services, community organizations, and policymakers to respond more effectively to local needs. By supporting continuous learning and adaptation, this approach may improve the scalability and sustainability of prevention strategies. If successful, the framework will not only generate actionable insights for Springfield but will also provide a transferable model for other communities seeking to operationalize systems-based approaches to chronic disease prevention at scale.

Greater Springfield has several unique characteristics, including its master-planned design, rapid population growth, and integrated urban infrastructure, which may facilitate the implementation of coordinated, place-based approaches to health. These contextual factors may not be directly replicable in all settings. However, the underlying principles of the Springfield Healthy Hearts Data Framework, specifically the integration of multilevel data, coordinated action across sectors, and iterative learning within a real-world context, are likely to be transferable to other communities. Adaptation to the local context will be essential, and future work will explore how this framework can be applied in settings with different demographic, geographic, and organizational characteristics.

The sustainability of the Springfield Healthy Hearts Data Framework is supported by its integration into existing community, health service, and partner systems. The Living Lab approach facilitates alignment with local priorities and ongoing stakeholder engagement, which is critical for long-term implementation. While initial development is supported by research funding, future sustainability will be supported by a combination of continued research investment, partnerships with government and service providers, and, where feasible, integration of data activities into routine practice. This approach aims to embed the framework within the local system, rather than maintaining it as a standalone research initiative.