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. 2026 Jun 3;22(2):18911803261458193. doi: 10.1177/18911803261458193

Barriers and Facilitators to the Implementation of the Diabetes Prevention Program (DPP) in Health Settings: Protocol for a Mixed Methods Systematic Review

Miquel Colom-Rosselló 1,2, Manuela Abbate 1,2,3,✉, Laura Capitán-Moyano 1,2,3, Elena Pastor-Ramon 4, Maria E Fernández 5,6,7, Aina M Yañez 1,2,3,8, Miquel Bennasar-Veny 1,2,3,9
PMCID: PMC13251839  PMID: 42282098

Abstract

This is the protocol of a Campbell Systematic Review. The objectives are as follows: to identify implementation determinants from the perspectives of both participants and implementers, and to assess their influence on specific implementation outcomes, thereby clarifying why certain aspects of the implementation process succeed or fail. If sufficient data is available, variations in perceived barriers or facilitators related to individuals and program characteristics will also be explored.

Keywords: Diabetes, Prediabetes, Diabetes Prevention Program, Barriers, Enablers, Facilitators, Implementation, Integrated Behavior Model, Consolidated Framework for Implementation Research

Background

Type 2 diabetes (T2D) is a global public health problem ranking as one of the leading causes of morbidity and mortality among adults due to its continuously rising prevalence over recent decades (Magliano & Boyko, 2021). Currently, 10.5% of adults aged 20–79 years are affected by diabetes worldwide, representing 537 million individuals. By 2030, this prevalence is estimated to increase to 11.3% (643 million), and by 2045 to 12.2% (783 million) (Sun et al., 2022). Furthermore, approximately 50% of individuals with T2D remain undiagnosed globally (Standl et al., 2019). This increasing prevalence of T2D is largely driven by ageing populations, economic growth and urbanization, which promote sedentary lifestyles and unhealthy dietary habits, ultimately leading to a higher incidence of obesity (Khan et al., 2020). As a predominantly lifestyle-related condition, T2D can be reversed, delayed, or prevented through targeted interventions that encourage healthier eating and increased physical activity (Hemmingsen et al., 2017). Such interventions offer the potential to mitigate global healthcare costs, which have already reached $966 billion/year (Magliano & Boyko, 2021).

The effectiveness of lifestyle interventions in preventing and delaying the onset of T2D has been well-established through numerous clinical trials (Aziz et al., 2015; Balk et al., 2015; Galaviz et al., 2018; Lindstrom et al., 2003, 2013; Stevens et al., 2015). One of the largest and most influential of these studies is the Diabetes Prevention Program (DPP) conducted in the United States (Diabetes Prevention Program Research et al., 2009). The standard program includes 16 core modules delivered in weekly sessions during the first six months, followed by monthly sessions for the remainder of the year.

The DPP showed that an intensive lifestyle intervention promoting healthy eating and increased physical activity resulted in a 58% reduction in T2D incidence after 2.8 years and a 34% reduction after 10 years of follow-up among at-risk individuals who completed the program. Program adherence was a crucial factor for achieving these intended outcomes as a minimum of eight sessions in the first six months of the intervention was associated with a greater weight loss (Ali et al., 2012; Ely et al., 2017).

Despite robust evidence, the implementation of evidence-based lifestyle interventions in real-world clinical practice remains insufficient (Aziz et al., 2015; Davies et al., 2017; Sanchez et al., 2018). A major challenge lies in identifying and overcoming the contextual barriers that inhibit the integration of these evidence-based interventions in often complex, dynamic and unpredictable settings (Harvey, 2022). Commonly reported barriers impeding the successful integration of T2D prevention programs, such as the DPP, into everyday practice include time constraints, heavy workloads, insufficient incentives, limited resources, and a predominant focus on disease treatment over prevention (Sanchez et al., 2012, 2018). To overcome these challenges, successful implementation requires a proactive approach, including strong leadership (Bianchi et al., 2018), active participation of healthcare professionals in collaborative processes, external facilitation (Cranley et al., 2017), tailoring interventions to local contexts (Powell et al., 2017; Wensing et al., 2010), and well-trained and knowledgeable staff (Weiss et al., 2016). However, despite these identified strategies, there remains limited evidence on how best to engage healthcare professionals in collaborative innovation processes, particularly within primary care settings where work overload, overcrowding, and differences in professionals’ attitudes pose significant challenges (Morgan et al., 2015).

To address the complex nature of implementing T2D prevention programs such as the DPP, implementation and behavioral science frameworks offer valuable tools for identifying and addressing key determinants. Frameworks like the Consolidated Framework for Implementation Research (CFIR) (Damschroder et al., 2022) and the Integrated Behavior Model (IBM) (Montaño & Kasprzyk, 2015) provide structured approaches to analyze contextual, organizational, and individual-level determinants that influence the successful implementation of evidence-based interventions (Lott et al., 2020; Weir et al., 2019).

The CFIR framework, originally published in 2009 and updated in 2022 based on user feedback, serves as a tool used for systematically identifying and addressing contextual factors influencing the implementation and sustainability of programs in real-world settings. It organizes 48 constructs and 19 subconstructs into five domains: (1) innovation: It focuses on the characteristics of the “thing” being implemented (e.g., the DPP); (2) outer setting: It includes external influences on implementation (e.g., patients’ needs and resources); (3) inner setting: It assesses organizational factors within the implementing institution (e.g., culture); (4) individuals: It describes roles and characteristics of individuals involved in the implementation process including the intervention recipients (e.g., self-efficacy); and (5) implementation process: It focuses on the strategies and activities undertaken to adopt, implement, and sustain the intervention (e.g., planning) (Damschroder et al., 2022).

In parallel, the Integrated Behavior Model (IBM) (Montaño & Kasprzyk, 2015) focuses more specifically on individual-level behavioral determinants. It integrates constructs from the Theory of Reasoned Action and the Theory of Planned Behavior to explain how attitudes, perceived norms, and self-efficacy influence behavioral intentions (Montaño & Kasprzyk, 2015). This theoretical framework can be used to complement the CFIR by further addressing individual-level determinants that inform individual’s intention and readiness to perform a behavior at both recipient and implementer level (Srinivasan et al., 2020).

Additionally, implementation science frameworks, such as RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) offer structured approaches to identify and evaluate implementation outcomes that influence implementation success or failure. The RE-AIM assesses multiple aspects of implementation through five dimensions: (1) Reach (i.e., who participated and why); (2) Effectiveness (i.e., impact of the intervention on outcomes); (3) Adoption (i.e., who delivered it and why); (4) Implementation (i.e., extent to which the intervention is delivered as intended); and (5) Maintenance (i.e., extent to which the intervention and its effects are sustained over time) (Glasgow et al., 1999, 2019).

Understanding facilitators and barriers to program implementation across diverse healthcare systems using implementation and behavioral science frameworks can help inform more tailored and effective strategies for implementing, scaling-up and sustaining diabetes prevention interventions. In this review, the CFIR will serve as the primary framework to identify and classify implementation determinants (barriers and facilitators) reported in the included studies. The RE-AIM framework will be used to organise and interpret implementation outcomes. The IBM will only be considered as a complementary framework if individual-level behavioural determinants relevant to implementation emerge that are not adequately captured within the CFIR framework. To our knowledge, no systematic review has integrated CFIR and RE-AIM to map determinants to implementation outcomes, with only a few primary studies adopting this approach (Damschroder et al., 2017; Madrigal et al., 2023). By directly linking determinants to the outcomes they influence, this integrated framework provides a structured approach to informing outcome-specific implementation strategies.

This systematic review is part of the ALADIM study, which aims to evaluate the effectiveness and implementation of an adapted DPP in the Spanish Primary Care setting. The study consists of two phases: Phase 1 involves adapting the DPP to the specific context of Spanish primary care, while Phase 2 assesses the effectiveness and implementation of the adapted program.

Within this process, the findings of this review will inform Task 1 of the Implementation Mapping approach, which focuses on identifying barriers and facilitators influencing implementation in the target context, thereby guiding the planning of the ALADIM project’s implementation phase. More broadly, the review supports the development, adaptation, and integration of diabetes prevention programs in healthcare systems by highlighting key facilitators to leverage and critical barriers to avoid for successful DPP implementation.

Objectives

The main objective of this review will be to identify implementation determinants from the perspectives of both participants and implementers, and to assess their influence on specific implementation outcomes, thereby clarifying why certain aspects of the implementation process succeed or fail. If sufficient data is available, variations in perceived barriers or facilitators related to individuals and program characteristics will also be explored.

Methods

This protocol is prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P) reporting guidelines (Shamseer et al., 2015), and has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Ref. No: CRD42024595128, available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=595128).

The review will adopt a ‘best-fit’ framework synthesis approach, which includes selecting the most appropriate framework to guide the synthesis (Carroll et al., 2013; Dixon-Woods, 2011). The CFIR has been chosen as the a priori framework (Damschroder et al., 2009, 2022) as it provides a structured approach to coding data and identifying constructs that influence the implementation of the DPP in healthcare settings, at both the individual and organizational levels. Constructs from this framework can serve as either a barrier or facilitator to implementation, negatively or positively affecting the process.

Criteria for Considering Studies for This Review

The eligibility criteria were developed using the SPIDER elements (Sample, Phenomenon of interest, Design, Evaluation, Research type) (Cooke et al., 2012).

Inclusion Criteria

  • • Sample - Studies involving implementers or people who have influence and/or power over the outcome of implementation efforts (e.g., lifestyle coaches, program coordinators, program providers, organization managers, healthcare professionals [medical doctors and nurses], or any personnel involved in any aspect of the implementation of the DPP within the organization), or the program participants.

  • • Phenomenon of Interest - Studies that describe determinants acting as barriers or facilitators to the successful implementation of the DPP or any DPP-based program in healthcare settings.

  • • Design and Research type - Primary studies using qualitative (case studies, phenomenology, ethnography, grounded theory, and action research), quantitative (cross-sectional, prospective, case-control, randomized and nonrandomized controlled trials), or mixed-method designs. Data collection methods include, but are not limited to focus groups, discussions, interviews, observations, in-depth or key-informant interviews, cross-sectional surveys, and questionnaires. No restrictions or limits will be applied regarding language, year of publication, or geographical location. When non-English studies are identified, titles and abstracts will be screened using translation tools when necessary.

  • • Evaluation - Studies reporting experiences, views, opinions, attitudes, perceptions, perspectives, beliefs, feelings, knowledge that act as barriers and facilitators to the implementation of the DPP or any DPP-based program in health settings.

Exclusion Criteria

Studies will be excluded if they focus on DPP lifestyle change programs delivered exclusively online or via distance learning (i.e., without any in-person component), as implementation determinants associated with fully digital delivery models may differ substantially from those influencing programs implemented within healthcare organisations (Johnson et al., 2025). Hybrid or mixed delivery formats combining in-person and digital components will remain eligible for inclusion. DPP-based programs that do not meet the minimum curriculum standards set by the Centers for Disease Control and Prevention (CDC) (Centers for Disease Control and Prevention, 2024) (i.e., 16 core modules delivered over at least 16 weekly sessions during months 1-6, and 6 modules over at least 6 monthly sessions during months 6-12), and lifestyle change prevention programs not DPP-based will also be excluded. Review articles, protocols, letters, commentaries, and editorials will be excluded as they do not report original empirical data. Finally, conference abstracts, proceedings, workshop reports, and government documents will also be excluded as they typically lack sufficient methodological detail and complete data sets required for a rigorous risk of bias assessment and formal data extraction.

Search Methods for Identification of Studies

The proposed search strategy is the result of an iterative process that started with a scoping search based upon the review question and eligibility criteria (Zwakman et al., 2018). From this initial search, relevant articles were used to extract keywords and index terms (including controlled vocabulary such as MeSH and database-specific subject headings), which were incorporated into the search strategy for refinement. The reference lists of these articles were also scanned for new citations, which, if relevant, contributed to new keywords, index terms, or to generally enhancing the understanding around the key elements of the research question. This process led to the refinement of the research question and eligibility criteria, as well as the identification of key articles that met eligibility criteria and should be included in the review. These articles were then used in the validation test of the search strategy, which was refined until all key articles were successfully identified.

Table 1 presents the keywords that will be used in the literature searches. As most major databases use distinct controlled vocabularies for article indexing, these terms will be adapted as needed to fit each individual database (Supplemental Table 1).

Table 1.

Search Strategy Keywords

Search no. Search terms
1 “Diabetes Prevention Program”
2 Barrier* OR difficult* OR impediment* OR issue* OR challenge* OR obstacle* OR facilitat* OR enabler* OR motivat* OR attitude* OR knowledge* OR perception* OR view* OR feeling* OR belief* OR opinion* OR perspective* OR determinant* OR factor*
3 Patient* OR participant* OR individual* OR subject* OR service user* OR end user* OR beneficiar*
4 “Health Personnel” OR “Community Health Workers” OR “General Practitioners” OR implementer* OR stakeholder* OR ((program OR Intervention) AND (staff OR provider* OR deliverer* OR adopter*)) OR “health worker*” OR “health professional*” OR nurses OR physician OR “lifestyle coach*” OR clinician* OR pharmacist* OR “local authority*” OR “clinic manager*” OR “Program commissioner*” OR “program coordinator*” OR “decision maker*” OR “healthcare provider*” OR ((health center OR health care OR Hospital) AND (manager* OR coordinator* OR administrator*)) OR ((Medical OR Nurs*) AND director)
5 Effectiveness OR Adoption OR Maintenance OR Reach OR engagement OR efficacy OR adherence OR participation OR enrollment OR endorsement OR uptake OR Implementation OR execution OR application
6 3 OR 4
7 1 AND 2 AND 5 AND 6
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Electronic Searches

Studies will be identified by searching the following databases: MEDLINE (via PubMed), CINAHL (via EBSCO), PsycINFO, Embase, Scopus, and Web of Science.

No database limits or filters (e.g., language, publication year, or study design filters) will be applied in order to maximize the sensitivity of the search strategy. Eligibility criteria related to study design will be applied during the screening stage.

Searching Other Resources

Supplementary searches for unpublished and grey literature will be conducted. Google Scholar will be searched using a simplified keyword strategy, and the first several hundred results sorted by relevance will be screened to identify potentially relevant studies not captured by the structured searches in bibliographic databases.

Grey literature will also be searched using Grey Matters (https://greymatters.cda-amc.ca), and ProQuest Dissertations and Theses Global to identify relevant empirical studies not indexed in bibliographic databases. Furthermore, to ensure literature saturation, the reference lists of the included studies and relevant review articles will be screened for any additional publications not identified in the database searches. Finally, targeted handsearching of key journals in the field (e.g., Implementation Science and BMC Public Health) will be conducted.

Data Collection and Analysis

Selection of Studies

Search results will be imported into Covidence, a web-based software platform used for managing and streamlining systematic reviews (covidence.org). Duplicates will be automatically removed. Initially, two reviewers (MC and MA) will independently screen title and abstract of the studies based on the predefined inclusion and exclusion criteria. Each study will be judged as either “not meeting the eligibility criteria” or “potentially meeting the eligibility criteria” for inclusion. The full text of potentially eligible studies will then be screened by the same two reviewers independently. Any discrepancies will be discussed and, if necessary, resolved with the help of a third expert reviewer (MBV), who will make the final decision regarding inclusion. The selection process will be illustrated using the PRISMA flow diagram, covering the following four main steps: identification, screening, eligibility, and inclusion (Figure 1).

Figure 1.

Figure 1.

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Flow Diagram of the Article Screening and Selection Process

Data Extraction and Management

The included studies will be uploaded into ATLAS.ti, a qualitative data analysis software package used to systematically analyze qualitative data (atlasti.com). Two reviewers (MC and LCM) will independently code data according to a pre-established extraction form/codebook. Any discrepancies between the reviewers will be discussed and resolved by consensus, and if necessary, a third reviewer (MBV) will be consulted. The extracted/coded data will include the following:

  • • Study information: study number, title, first author and year of publication, country/region of study

  • • Study design and methodology: design, sampling and sample size, data collection methods (e.g., interviews, focus groups, observations), data analysis techniques, and any theoretical frameworks used to interpret or contextualize the findings.

  • • Participants’ characteristics: sample size, participant type (e.g., implementers such as adopters, program coordinators, and deliverers, or program participants), and age.

  • • Program characteristics: frequency and total number of sessions, mode of delivery (e.g., combination of online and in-person; in-person only), program length, healthcare setting (e.g., clinic, hospital, primary care) and any adaptations made to the original DPP, including the specific components adapted (e.g., content, delivery, intensity).

  • • Reported determinants identified as barriers or facilitators
    • - captured by the CFIR constructs.
    • - not captured by any CFIR constructs but deemed relevant by the reviewers upon agreement.

To facilitate coding consistency and reliability, a coding manual with the definitions of 39 constructs of the 5 CFIR domains will be provided to the reviewers (Supplemental Table 2).

Assessment of Risk of Bias in Included Studies

The methodological quality of the included studies will be assessed using the Mixed Methods Appraisal Tool Version 2018 (MMAT) (Hong, Fàbregues, et al., 2018; Hong, Gonzalez-Reyes et al., 2018), a comprehensive tool suitable for evaluating qualitative, quantitative, and mixed-method studies (Supplemental Table 3). It evaluates studies across five types of research designs, with each type having its own set of criteria. The research designs are: Qualitative studies, quantitative-randomized controlled trials, quantitative-nonrandomized trials, quantitative-descriptive studies, and mixed-method studies. Each study will be evaluated against the five criteria specific to its methodological design. Responses for each item will be rated as “Yes,” “No,” or “Can’t tell.” Overall quality will be expressed as the percentage of criteria met, ranging from 0% (no criteria met) to 100% (all five criteria met), providing a general indication of methodological rigor.

Prior to the formal quality assessment process, two reviewers (MC and MA) will pilot test the MMAT tool to ensure agreement in scoring. Then, the same two reviewers will independently score each study, document the rationale for their scores, and reference the relevant sections of the full-text articles to ensure consistency. Any discrepancies will be discussed, and in case of disagreement, a third reviewer (AMY) will be consulted. No study will be excluded based solely on its quality assessment score. Instead, methodological quality will be considered in the interpretation of the findings to allow a descriptive assessment of methodological strengths and limitations, and sensitivity analyses will be conducted, where feasible, to explore whether patterns of reported barriers and facilitators differ when excluding low-quality studies.

Dealing With Missing Data

Where essential information is missing (e.g., details of study design, setting, or relevant results), we will attempt to contact the corresponding authors for clarification. If data remain unavailable, we will report this in the ‘Characteristics of included studies’ tables and consider its implications when interpreting the results. We will not use statistical methods to impute missing data, as our synthesis will be narrative.

Data Synthesis

This systematic review follows a convergent synthesis design where data extracted from the qualitative, quantitative and mixed-method studies are analyzed in parallel using a unified synthesis method, with the results presented together (Hong et al., 2017). Quantitative data obtained from surveys or questionnaires, as well as the quantitative components of mixed-method studies will be trans-formed through narrative interpretation into descriptive textual summaries. During this process, numerical findings (e.g., frequencies, proportions, or survey results) will be translated into textual descriptions that preserve the direction and relative magnitude of the reported findings to ensure that their contribution to the synthesis remains transparent. These ‘qualitized’ data were subsequently mapped onto the predefined outcomes and constructs of the RE-AIM and CFIR frameworks (Hong et al., 2017).

Data analysis will use a hybrid thematic approach, combining deductive and inductive analysis. Initially, the reviewers’ interpretations regarding barriers and facilitators to implementation will be mapped into the 39 constructs across the five CFIR domains. If a reported barrier or enabler fits more than one domain, it will be coded accordingly in multiple domains to capture the multidimensional nature of implementation determinants.

Any data that does not fit within the predetermined constructs will undergo inductive analysis and will be handled in a flexible manner. Initially, through a consensus exercise among the review authors, similar data will be grouped based on their content or meaning. For each data group, a concise label will be collaboratively created to summarize the common theme expressed by the data. Finally, data will either be integrated into the most closely related pre-existing constructs or new themes or sub-themes will be created and integrated into an expanded framework (Carroll et al., 2011). If a substantial number of individual-level barriers and facilitators are identified that do not clearly map onto CFIR constructs, particularly those related to behavioral intention or readiness, the IBM may be incorporated to support the classification of these determinants within a complementary theoretical framework.

Following the identification and classification of determinants using CFIR, identified barriers and facilitators will be mapped into the key dimensions (i.e., indicators) of the RE-AIM implementation framework. This approach will help elucidate how contextual factors and individual influences relate to specific outcomes associated with implementation success. In addition to narrative synthesis, the consistency of reported determinants across studies will be considered to highlight the most frequently identified barriers and facilitators. This synthesis approach will provide a deeper understanding of the real-world complexities surrounding the success of DPP implementation.

If sufficient data are available, exploratory comparisons will be conducted to explore whether patterns of reported barriers and facilitators vary according to relevant study characteristics (e.g., healthcare setting, country, program characteristics). Comparisons will be conducted descriptively based on the information reported in the included studies.

Anticipated Limitations

This review may have several limitations. The heterogeneity of study designs and variability in the reporting of implementation outcomes may limit the comparability and transferability of findings. In addition, many studies may rely on self-reported data, which may introduce reporting bias. Although coding will be conducted through consensus procedures, some subjectivity may remain. Finally, the exclusion of fully digital programmes may limit the applicability of the findings to emerging digital delivery models of diabetes prevention interventions.

Supplemental Material

Supplemental Material - Barriers and Facilitators to the Implementation of the Diabetes Prevention Program (DPP) in Health Settings: Protocol for a Mixed Methods Systematic Review
sj-pdf-1-cam-10.1177_18911803261458193.pdf (473.4KB, pdf)

Supplemental Material for Barriers and Facilitators to the Implementation of the Diabetes Prevention Program (DPP) in Health Settings: Protocol for a Mixed Methods Systematic Review by Miquel Colom-Rosselló, Manuela Abbate, Laura Capitán-Moyano, Elena Pastor-Ramon, Maria E. Fernández, Aina M. Yañez, Miquel Bennasar-Veny in Campbell Systematic Reviews

Acknowledgements

We would like to acknowledge the ALADIM study group: Aina Huguet-Torres, Ivonne Carolina Hernández-Bermúdez, Escarlata Angullo, and Isabel Socias. MC-R has a PFIS predoctoral contract (FI24/0180) funded by the Instituto de Salud Carlos III (ISCIII) and co-financed by the European Social Fund Plus (ESF+). MA has a Margalida Comas Postdoctoral contract (PD_048_2023) co-financed by the Balearic Government and by the European Social Fund (ESF+) for the period 2021-2027.

Author Contributions: All authors were involved in the conceptualization of the review and editing of the protocol. MC was primarily responsible for protocol writing. MC, MA and EPR will be responsible for searching the literature and data management. Both MC and LC will be responsible for article screening, quality assessment, coding and analysis with input and guidance from MEF, AMY and MBV. All authors contributed to and approved the final protocol manuscript.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Instituto de Salud Carlos III; PI23/01625.

The authors declare they have no competing interests.

Supplemental Material: Supplemental material for this article is available online.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analysed in this study. All materials supporting the protocol (search strategy, draft data extraction forms, and analysis plan) are available from the corresponding author upon reasonable request and will be made publicly accessible in the published review.*

References

  1. Ali M. K., Echouffo-Tcheugui J., Williamson D. F. (2012). How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program? Health Affairs, 31(1), 67–75. 10.1377/hlthaff.2011.1009 [DOI] [PubMed] [Google Scholar]
  2. Aziz Z., Absetz P., Oldroyd J., Pronk N. P., Oldenburg B. (2015). A systematic review of real-world diabetes prevention programs: Learnings from the last 15 years. Implementation Science, 10(1), 172. 10.1186/s13012-015-0354-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Balk E. M., Earley A., Raman G., Avendano E. A., Pittas A. G., Remington P. L. (2015). Combined diet and physical activity promotion programs to prevent type 2 diabetes among persons at increased risk: A systematic review for the community preventive services task force. Annals of Internal Medicine, 163(6), 437–451. 10.7326/M15-0452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bianchi M., Bagnasco A., Bressan V., Barisone M., Timmins F., Rossi S., Pellegrini R., Aleo G., Sasso L. (2018). A review of the role of nurse leadership in promoting and sustaining evidence-based practice. Journal of Nursing Management, 26(8), 918–932. 10.1111/jonm.12638 [DOI] [PubMed] [Google Scholar]
  5. Carroll C., Booth A., Cooper K. (2011). A worked example of “best fit” framework synthesis: A systematic review of views concerning the taking of some potential chemopreventive agents. BMC Medical Research Methodology, 11(1), 29. 10.1186/1471-2288-11-29 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Carroll C., Booth A., Leaviss J., Rick J. (2013). “Best fit” framework synthesis: Refining the method. BMC Medical Research Methodology, 13, 37. 10.1186/1471-2288-13-37 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Centers for Disease Control and Prevention . (2024). Centers for Disease Control and Prevention Diabetes Prevention Recognition Program. https://www.cdc.gov/diabetes-prevention/media/pdfs/legacy/dprp-standards.pdf [Google Scholar]
  8. Cooke A., Smith D., Booth A. (2012). Beyond PICO: The SPIDER tool for qualitative evidence synthesis. Qualitative Health Research, 22(10), 1435–1443. 10.1177/1049732312452938 [DOI] [PubMed] [Google Scholar]
  9. Cranley L. A., Cummings G. G., Profetto-McGrath J., Toth F., Estabrooks C. A. (2017). Facilitation roles and characteristics associated with research use by healthcare professionals: A scoping review. BMJ Open, 7(8), Article e014384. 10.1136/bmjopen-2016-014384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Damschroder L. J., Aron D. C., Keith R. E., Kirsh S. R., Alexander J. A., Lowery J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, 50. 10.1186/1748-5908-4-50 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Damschroder L. J., Reardon C. M., AuYoung M., Moin T., Datta S. K., Sparks J. B., Maciejewski M. L., Steinle N. I., Weinreb J. E., Hughes M., Pinault L. F., Xiang X. M., Billington C., Richardson C. R. (2017). Implementation findings from a hybrid III implementation-effectiveness trial of the Diabetes Prevention Program (DPP) in the Veterans Health Administration (VHA). Implementation Science, 12(1), 94. 10.1186/s13012-017-0619-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Damschroder L. J., Reardon C. M., Widerquist M. A. O., Lowery J. (2022). The updated Consolidated Framework for Implementation Research based on user feedback. Implementation Science, 17(1), 75. 10.1186/s13012-022-01245-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Davies M. J., Gray L. J., Ahrabian D., Carey M., Farooqi A., Gray A., Goldby S., Hill S., Jones K., Leal J., Realf K., Skinner T., Stribling B., Troughton J., Yates T., Khunti K. (2017). A community-based primary prevention programme for type 2 diabetes mellitus integrating identification and lifestyle intervention for prevention: A cluster randomised controlled trial. NIHR Journals Library, 5, 1–290. (Programme Grants for Applied Research). 10.3310/pgfar05020 [DOI] [PubMed] [Google Scholar]
  14. Diabetes Prevention Program Research G., Knowler W. C., Fowler S. E., Hamman R. F., Christophi C. A., Hoffman H. J., Brenneman A. T., Brown-Friday J. O., Goldberg R., Venditti E., Nathan D. M. (2009). 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet, 374(9702), 1677–1686. 10.1016/S0140-6736(09)61457-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dixon-Woods M. (2011). Using framework-based synthesis for conducting reviews of qualitative studies. BMC Medicine, 9, 39. 10.1186/1741-7015-9-39 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ely E. K., Gruss S. M., Luman E. T., Gregg E. W., Ali M. K., Nhim K., Rolka D. B., Albright A. L. (2017). A national effort to prevent type 2 diabetes: Participant-level evaluation of CDC's National Diabetes Prevention Program. Diabetes Care, 40(10), 1331–1341. 10.2337/dc16-2099 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Galaviz K. I., Weber M. B., Straus A., Haw J. S., Narayan K. M. V., Ali M. K. (2018). Global diabetes prevention interventions: A systematic review and network meta-analysis of the real-world impact on incidence, weight, and glucose. Diabetes Care, 41(7), 1526–1534. 10.2337/dc17-2222 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Glasgow R. E., Harden S. M., Gaglio B., Rabin B., Smith M. L., Porter G. C., Ory M. G., Estabrooks P. A. (2019). RE-AIM planning and evaluation framework: Adapting to new science and practice with a 20-Year review. Frontiers in Public Health, 7, 64. 10.3389/fpubh.2019.00064 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Glasgow R. E., Vogt T. M., Boles S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89(9), 1322–1327. 10.2105/ajph.89.9.1322 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Harvey G. (2022). Context matters, so how do we get better at working with context in implementation research and practice? Comment on “Stakeholder perspectives of attributes and features of context relevant to knowledge translation in health settings: A multi-country analysis”. International Journal of Health Policy and Management, 11(8), 1587–1589. 10.34172/ijhpm.2022.7088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hemmingsen B., Gimenez-Perez G., Mauricio D., Roque I. F. M., Metzendorf M. I., Richter B. (2017). Diet, physical activity or both for prevention or delay of type 2 diabetes mellitus and its associated complications in people at increased risk of developing type 2 diabetes mellitus. Cochrane Database of Systematic Reviews, 12(12), Article CD003054. 10.1002/14651858.CD003054.pub4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hong Q. N., Fàbregues S., Bartlett G., Boardman F., Cargo M., Dagenais P., Gagnon M.-P., Griffiths F., Nicolau B., O’Cathain A., Rousseau M.-C., Vedel I., Pluye P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. 10.3233/efi-180221 [DOI] [Google Scholar]
  23. Hong Q. N., Gonzalez‐Reyes A., Pluye P. (2018). Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the Mixed Methods Appraisal Tool (MMAT). Journal of Evaluation in Clinical Practice, 24(3), 459–467. 10.1111/jep.12884 [DOI] [PubMed] [Google Scholar]
  24. Hong Q. N., Pluye P., Bujold M., Wassef M. (2017). Convergent and sequential synthesis designs: Implications for conducting and reporting systematic reviews of qualitative and quantitative evidence. Systematic Reviews, 6(1), 61. 10.1186/s13643-017-0454-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Johnson S. S., Castle P. H., Bosack S. (2025). Leveraging technology and theory to change health behaviors, close gaps in health-related social needs, and increase enrollment in the National Diabetes Prevention Program. Preventing Chronic Disease, 22, Article 240284. 10.5888/pcd22.240284 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Khan M. A. B., Hashim M. J., King J. K., Govender R. D., Mustafa H., Al Kaabi J. (2020). Epidemiology of type 2 diabetes - Global burden of disease and forecasted trends. J Epidemiol Glob Health, 10(1), 107–111. 10.2991/jegh.k.191028.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lindstrom J., Louheranta A., Mannelin M., Rastas M., Salminen V., Eriksson J., Uusitupa M., Tuomilehto J., Finnish Diabetes Prevention Study G. (2003). The Finnish Diabetes Prevention Study (DPS): Lifestyle intervention and 3-year results on diet and physical activity. Diabetes Care, 26(12), 3230–3236. 10.2337/diacare.26.12.3230 [DOI] [PubMed] [Google Scholar]
  28. Lindstrom J., Peltonen M., Eriksson J. G., Ilanne-Parikka P., Aunola S., Keinanen-Kiukaanniemi S., Uusitupa M., Tuomilehto J., Finnish Diabetes Prevention S. (2013). Improved lifestyle and decreased diabetes risk over 13 years: Long-term follow-up of the randomised Finnish Diabetes Prevention Study (DPS). Diabetologia, 56(2), 284–293. 10.1007/s00125-012-2752-5 [DOI] [PubMed] [Google Scholar]
  29. Lott B. E., Trejo M. J., Baum C., McClelland D. J., Adsul P., Madhivanan P., Carvajal S., Ernst K., Ehiri J. (2020). Interventions to increase uptake of cervical screening in Sub-Saharan Africa: A scoping review using the integrated behavioral model. BMC Public Health, 20(1), 654. 10.1186/s12889-020-08777-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Madrigal L., Haardorfer R., Kegler M. C., Piper S., Blais L. M., Weber M. B., Escoffery C. (2023). A structural equation model of CFIR inner and outer setting constructs, organization characteristics, and national DPP enrollment. Implement Sci Commun, 4(1), 142. 10.1186/s43058-023-00522-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Magliano D. J., Boyko E. J. (2021). IDF diabetes atlas (10th ed.). International Diabetes Federation. https://www.ncbi.nlm.nih.gov/pubmed/35914061 [Google Scholar]
  32. Montaño D. E., Kasprzyk D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In Health behavior: Theory, research, and practice (pp. 95–124). John Wiley & Sons. [Google Scholar]
  33. Morgan S., Pullon S., McKinlay E. (2015). Observation of interprofessional collaborative practice in primary care teams: An integrative literature review. International Journal of Nursing Studies, 52(7), 1217–1230. 10.1016/j.ijnurstu.2015.03.008 [DOI] [PubMed] [Google Scholar]
  34. Powell B. J., Beidas R. S., Lewis C. C., Aarons G. A., McMillen J. C., Proctor E. K., Mandell D. S. (2017). Methods to improve the selection and tailoring of implementation strategies. The Journal of Behavioral Health Services & Research, 44(2), 177–194. 10.1007/s11414-015-9475-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Sanchez A., Silvestre C., Campo N., Grandes G., Pred D. E. G. (2018). Effective translation of a type-2 diabetes primary prevention programme into routine primary care: The PreDE cluster randomised clinical trial. Diabetes Research and Clinical Practice, 139, 32–42. 10.1016/j.diabres.2018.01.006 [DOI] [PubMed] [Google Scholar]
  36. Sanchez A., Silvestre C., Sauto R., Martinez C., Grandes G., Pre D. E. r. g. (2012). Feasibility and effectiveness of the implementation of a primary prevention programme for type 2 diabetes in routine primary care practice: A phase IV cluster randomised clinical trial. BMC Family Practice, 13, 109. 10.1186/1471-2296-13-109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Shamseer L., Moher D., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L. A., Group P.-P. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: Elaboration and explanation. BMJ, 349, g7647. 10.1136/bmj.g7647 [DOI] [PubMed] [Google Scholar]
  38. Srinivasan S., Hampel H., Leeman J., Patel A., Kulchak Rahm A., Reuland D. S., Roberts M. C. (2020). Stakeholder perspectives on overcoming barriers to Cascade testing in Lynch Syndrome: A qualitative study. Cancer Prevention Research, 13(12), 1037–1046. 10.1158/1940-6207.CAPR-20-0141 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Standl E., Khunti K., Hansen T. B., Schnell O. (2019). The global epidemics of diabetes in the 21st century: Current situation and perspectives. European Journal of Preventive Cardiology, 26(2_suppl), 7–14. 10.1177/2047487319881021 [DOI] [PubMed] [Google Scholar]
  40. Stevens J. W., Khunti K., Harvey R., Johnson M., Preston L., Woods H. B., Davies M., Goyder E. (2015). Preventing the progression to type 2 diabetes mellitus in adults at high risk: A systematic review and network meta-analysis of lifestyle, pharmacological and surgical interventions. Diabetes Research and Clinical Practice, 107(3), 320–331. 10.1016/j.diabres.2015.01.027 [DOI] [PubMed] [Google Scholar]
  41. Sun H., Saeedi P., Karuranga S., Pinkepank M., Ogurtsova K., Duncan B. B., Stein C., Basit A., Chan J. C. N., Mbanya J. C., Pavkov M. E., Ramachandaran A., Wild S. H., James S., Herman W. H., Zhang P., Bommer C., Kuo S., Boyko E. J., Magliano D. J. (2022). IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Research and Clinical Practice, 183, Article 109119. 10.1016/j.diabres.2021.109119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Weir N. M., Newham R., Dunlop E., Bennie M. (2019). Factors influencing national implementation of innovations within community pharmacy: A systematic review applying the Consolidated Framework for Implementation Research. Implementation Science, 14(1), 21. 10.1186/s13012-019-0867-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Weiss D., Lillefjell M., Magnus E. (2016). Facilitators for the development and implementation of health promoting policy and programs - A scoping review at the local community level. BMC Public Health, 16, 140. 10.1186/s12889-016-2811-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wensing M., Bosch M., Grol R. (2010). Developing and selecting interventions for translating knowledge to action. Canadian Medical Association Journal, 182(2), E85–E88. 10.1503/cmaj.081233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Zwakman M., Verberne L. M., Kars M. C., Hooft L., van Delden J. J. M., Spijker R. (2018). Introducing PALETTE: An iterative method for conducting a literature search for a review in palliative care. BMC Palliat Care, 17(1), 82. 10.1186/s12904-018-0335-z [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material - Barriers and Facilitators to the Implementation of the Diabetes Prevention Program (DPP) in Health Settings: Protocol for a Mixed Methods Systematic Review
sj-pdf-1-cam-10.1177_18911803261458193.pdf (473.4KB, pdf)

Supplemental Material for Barriers and Facilitators to the Implementation of the Diabetes Prevention Program (DPP) in Health Settings: Protocol for a Mixed Methods Systematic Review by Miquel Colom-Rosselló, Manuela Abbate, Laura Capitán-Moyano, Elena Pastor-Ramon, Maria E. Fernández, Aina M. Yañez, Miquel Bennasar-Veny in Campbell Systematic Reviews

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analysed in this study. All materials supporting the protocol (search strategy, draft data extraction forms, and analysis plan) are available from the corresponding author upon reasonable request and will be made publicly accessible in the published review.*

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