Dietary risk factors are a leading cause of preventable death and disability (1). Reducing dietary risks is recommended to improve child health and mitigate future burdens of chronic disease (2). In Australia, for example, 96% of children do not consume sufficient serves of vegetables, while discretionary foods (i.e., foods high in added sugar, saturated fat and sodium) account for over one-third of children’s daily energy intake (3). Schools provide universal access to children aged over 5 years, and are a setting recommended for nutrition interventions in chronic disease prevention internationally (4–6). In countries such as Australia, food brought to school (from home) packed in school ‘lunchboxes’ are used daily by 90% of students, (7) and contribute up to 30–50% of a child’s daily energy intake (7). As approximately 40% of foods in lunchboxes are discretionary (8) improving the packing of healthy foods for child consumption at school provides a considerable opportunity for chronic disease prevention.

Systematic reviews suggest that school-based healthy lunchbox interventions can improve student nutritional intake (9). In Australia, a series of randomized controlled trials of a healthy lunchbox program, known as ‘SWAP IT’ were recently conducted in 34 primary schools with 4,600 children (10, 11). The program supports parents and carers to make simple ‘swaps’ aligned to dietary guidelines, (12) replacing discretionary food and beverage items with comparable core (nutrient dense) items. It is comprised of three broad program components: (i) school food (lunchbox) guidelines; (ii) messages and hard copy resources to parents and carers; and (iii) curricula resources for teachers. Across these randomized trials, the program was found to significantly improve child diet quality, energy intake and weight status, and was acceptable to both parents and teachers (10, 11). A subsequent comparative effectiveness randomized trial found no difference in effectiveness between the messages and parent booklets combined, compared with those two components plus school-based curriculum and policy resources on student dietary outcomes.

Given the reported benefits of SWAP IT on child health, (10, 11) broad implementation in schools has the potential to make a significant contribution to improving public health nutrition. An important impediment to the large scale adoption of effective school nutrition initiatives, however, is a lack of published evidence of effective strategies to implement them (13). A recent Cochrane review of implementation strategies for school-based health promotion programs identified few randomized controlled trials of strategies to implement policies and practices promoting healthy eating, particularly ‘at scale’ (defined by the authors as 50 or more schools) (13). Furthermore, strategies identified as effective in improving implementation in one jurisdiction (e.g., Local Health District), may not be effective, appropriate or feasible for application in another. Similarly, differing capacities (e.g., resources or infrastructure) of agencies responsible for undertaking or supporting program implementation may mean an effective implementation strategy in one jurisdiction may not be feasible to execute in another. Such issues must be addressed if effective interventions are to be adopted at a population level (at scale).

As in clinical services, systematic reviews and best practice guidelines identify evidence-based programs and practices that can be employed in community settings to reduce child dietary risks. As such, within devolved health systems such as Australia, different health services will often seek to address the same disease risk or health condition, using the same intervention (e.g., guideline concordant care) at the same time (14). These services, however, operate in different contexts, with different capacities and resource constraints. As a result, there is often natural heterogeneity in the strategies that health services employ to support the implementation of programs in schools and other clinical and community settings to improve dietary (and other) outcomes. This convergence of objective (to implement a similar intervention), but heterogeneity in context and strategies used to implement school-based programs, presents an attractive opportunity to learn about the types of implementation strategies that may be effective in different contexts. Specifically, the coordinated evaluation of implementation efforts across a network of health services, and the establishment of processes to share and learn from the findings, may provide a mechanism for rapid evidence generation, and health system improvement ‘at scale’. Such collaborative and data-driven models of working are also consistent with recommendations for the development of ‘learning health system’ approaches to healthcare improvement (15).

Broadly, Master Protocols represent an approach that could be used to facilitate coordinated and collaborative research, learning and improvement (16). Master Protocols refer to designs employing coordinated approaches to assess the effects of interventions within a unifying overall trial structure (16). This infrastructure, including a centralized trial protocol and governance, facilitates the standardization of study processes and procedures, including recruitment, evaluation and data collection, analysis, and reporting (17). Although frequently used to test pharmacological interventions, (18–20) this type of trial design is not broadly used within community-based interventions and to our knowledge, has not previously been used to assess the effectiveness of strategies on school implementation of health promotion programs. Employing this type of trial design would be a novel transformation from how health promotion programs, and strategies to support their implementation, are conventionally tested. Currently, few trials test the effectiveness of strategies to improve the implementation of such programs, (21) and those that do often employ different research designs and measures. This impedes cross-study synthesis, and also fails to address the issue of context, with strategies that effectively improve implementation in one context potentially unsuitable or ineffective in another (22). Comparatively, Master Protocol designs allow for the examination of multiple hypotheses, (23) such as the effects of a variety of implementation or scale-up strategies on school implementation of health promotion programs, or differences in effectiveness for different population groups.

Following demonstration of the effectiveness and acceptability of the SWAP IT program, (10, 11) three Local Health Districts (LHDs) from across New South Wales (NSW), Australia, expressed interest in supporting the implementation of this program in their LHD. In this context, and drawing on research design principles of Master Protocols and prospective meta-analysis methodology, (24) a pilot collaboration was formed that networked three LHDs and the University of Newcastle (National Centre of Implementation Science) (25) to undertake a harmonized evaluation of strategies used within each LHD to support the adoption of the SWAP IT program, and to share learning from these evaluations across participating LHDs (26). The collaboration was supported by shared implementation strategy development processes, governance structures, centralized data collection infrastructure, and a community of practice (26). While collaboration across and flexibility within LHDs for the implementation of various health promotion programs has occurred routinely among NSW LHDs over time, a formal evaluation of such a collaborative approach had not been undertaken. The pilot found the collaborative model was highly acceptable to all parties, (26) and strategies employed yielded significant, but contextually dependent improvements in program adoption.

Based on these encouraging findings, the collaborative approach is now being employed across 10 of the 15 LHDs (67%) in NSW. This paper describes the protocol for what we term a “Collaborative Network Trial” to support the simultaneous testing of different implementation strategies undertaken by 10 LHDs in NSW, Australia to facilitate the adoption of the SWAP IT program at scale.

As such, the primary objective of this study is to assess, using individual level participant (in this case ‘school’) data (IPD), the effectiveness of different implementation strategies employed by 10 NSW LHDs to increase school adoption of the SWAP IT program. Secondary objectives of the study are to: (1) explore the effects of different implementation strategy components and contextual factors on the school-level adoption of SWAP IT using pooled individual level data across all trials; (2) assess the acceptability of the implementation strategies to school principals; and (3) assess the sustainability of SWAP IT within schools that adopted the program at 18-months.

This protocol provides a comprehensive description of a novel research design, employing individual level participant (i.e., ‘school’) data component meta-analysis, to help generate evidence that can better inform approaches to support the adoption and implementation of health promotion interventions at scale. The study will provide rigorous evidence of the effects of a variety of implementation strategies, employed in different contexts on the adoption of the SWAP IT school lunchbox program.

Evidence generated from this research will help address an important constraint of the current literature, with systematic review evidence identifying few rigorous trials that have tested strategies to implement health promotion interventions at scale. The strategies tested within this study have been developed following a systematic co-design approach with implementation researchers, LHD health promotion staff and other stakeholders. In addition to considering the evidence-base (i.e., barriers and enablers to adoption of school-based programs, and the effectiveness of implementation strategies), this process included working with LHD health promotion staff to consider the human, technical and financial resources available in LHDs responsible for strategy delivery. Applying this type of systematic approach to scale up has been recommended by implementation and scale-up experts to help address a common pitfall of scale-up research, which is the diminishment in effect of interventions with proven efficacy when delivered at scale (42, 43).

The currently limited evidence base has resulted in a failure to provide guidance on the crucial issue of context, with strategies that effectively improve implementation in one context potentially ineffective or inappropriate to deliver in another (22). Through partnering with 13 NSW LHDs (including three from the pilot) to conduct this research, schools from all sectors and located within the majority (86%) of the state of NSW will be represented. These LHDs encompass socioeconomically and geographically diverse regions, ensuring the contexts in which these strategies are tested are diverse and representative of the broader setting (31). In order to further address the issue of context, future research should potentially consider identifying and addressing other contextual, sectoral and political factors that may be influential in maximizing school adoption of the SWAP IT program. For example, the World Health Organization’s Health Promoting Schools Framework recommends employing a comprehensive approach, encompassing education (e.g., learning and curriculum), environmental (e.g., culture and policies) and partnership (e.g., families, health professionals and educators, teachers and community) components, to enhance the effectiveness of health promotion programs (44). Employing individual-level participant data component meta-analysis within this research provides an opportunity to gather robust evidence on the types of strategies that are effective in improving implementation of SWAP IT, and in what context. Additionally, it addresses a noticeable constraint in the current literature, that is, the substantial heterogeneity in trial design and measures employed in the few studies that have tested strategies to implement health promotion programs at scale. IPD meta-analysis is considered the gold standard for combining data from randomized trials and has several advantages over other analytical approaches (45–47). These advantages include the increase in statistical power compared to aggregate data meta-analysis, the ability to standardize the analysis across studies to ensure consistency in outcome measures, and enhancing the ability to effectively explore heterogeneity in participant characteristics (i.e., schools and LHDs) and treatment effects (i.e., implementation strategies) (45–47). This type of analysis has been frequently employed to synthesize the effects of health behavior interventions (48–50). For example, the Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration uses IPD meta-analysis to assess the effectiveness of obesity-prevention interventions on child weight outcomes, and also assess differential effects by individual- and trial-level characteristics (50).

The use of objective and validated measures of data collection to assess study outcomes is a considerable strength of the study. For example, the objective measure of school adoption of SWAP IT, automatically captured upon school registration for the program, will provide high-quality and accurate assessment of the trial primary outcome. The use of validated measures (38) within the survey to assess school acceptability of the employed implementation strategies will provide reliable insight into the types of strategies that could potentially be employed within future interventions to support the implementation of health promotion programs. The use of such measures has been recommended by leading implementation researchers, who have developed definitions and validated measures of implementation outcomes (including adoption and acceptability) to improve the consistency in how outcomes are assessed within the implementation field and enable the comparison of strategies across studies (38, 51). These definitions and measures have been incorporated within other school-based interventions to assess implementation outcomes (52, 53). Despite the strengths outlined above, a number of limitations should be considered. While employing a Master Protocol trial design is innovative and shows promise as a method to transform traditional approaches to evaluating strategies to improve implementation of health promotion programs, there is also limited research to guide the conduct of such trials in school-based interventions. As such, the utility of this type of trial design in school-based interventions is still largely unknown. Indeed, the study will provide valuable learnings of this design as a model of evidence generation more broadly. Additionally, although the analysis will include schools from 13 of the 15 LHDs, these schools are solely located within one state of Australia. As such, generalizability of the findings beyond this region may be limited.

The studies involving humans were approved by Hunter New England (2019/ETH12353); University of Newcastle (09/07/26/4.04); NSW of Department of Education (2018247); and the Maitland-Newcastle, Sydney, Wollongong, Bathurst, Parramatta, Wagga Wagga and Canberra-Goulburn Catholic Dioceses. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

CB: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. JJ: Investigation, Methodology. Project administration, Writing – original draft, Writing – review & editing. LW: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. KR: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. AS: Formal analysis, Methodology, Writing – original draft, Writing – review & editing. JN: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. PB: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. MM: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. CP: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. LM: Writing – review & editing, Investigation, Methodology, Project administration, Writing – original draft. JM: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. JP: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. SM: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. SD: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. CV: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. ND: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. KM: Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. RS: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing.