There is a long history of developing and testing health promotion, disease prevention, and disease self-management (secondary prevention) interventions with the intent to have a public health impact (1, 2). As a result there are a myriad of efficacious interventions across these areas that have been compiled into registries to support broad dissemination and implementation (3). However, to achieve a public health impact, there is a need for these interventions to have broad reach and be effective. Reach is an individual-level dissemination outcome and can be defined as the number of participants that enroll, the proportion of eligible people exposed to recruitment activities that enroll, and the representativeness of those enrolled in a given health promotion intervention relative to the intended audience based on demographic characteristics (4). Further underscoring the need to address representativeness, public health goals also focus on increasing the reach of evidence-based interventions in populations that experience health disparities (5).

Within the field of dissemination and implementation science—where understanding the reach, adoption, implementation, and sustainment of evidence-based interventions is foundational—strategies that focus on improving the reach of evidence-based programs for all populations have increased in importance (5, 6). Unfortunately, over the previous 20 years, the degree to which intervention trials have reported on recruitment strategies, or compared strategies, to improve intervention reach have been sparse and what research does exist in this area has been limited (7, 8). In some cases, research has focused on potential participant enablers and barriers to participation (9). Other studies have examined recruitment only from the perspective of providers or physician referrals (10). Still others have examined recruitment relative to a single intervention structure (e.g., group-based) (11) or health behavior outcome (e.g., physical activity) (12).

When considering the reach of health behavior interventions, there are several factors that are hypothesized to determine success (13). These factors include the characteristics of the (1) intended audience, (2) delivery setting and staff, (3) intervention, (4) external factors, and the (5) strategies used to recruit participants (14, 15). Addressing each of these factors within a single study is impractical and the ability to examine potential interactions is likely only possible through a review of literature that has examined reach across a number of populations, delivery settings, intervention structures and foci, and recruitment strategies.

In addition, understanding the underlying mechanisms by which strategies to improve reach achieve a high and representative number and proportion of participants from the intended audience will advance scientific understanding and provide practical principles that can be used to develop additional successful strategies (16). The Practical, Robust, Implementation, and Sustainability Model and Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework (PRISM/RE-AIM) both provide useful guidance on identifying potential mechanisms and ways to operationalize intervention reach. Specifically, the RE-AIM Framework is one of the few dissemination and implementation science frameworks that provides direction on how best to operationalize reach with an emphasis on the number, proportion, and representativeness of participants who are exposed to a recruitment strategy, engage in the recruitment process, and are enrolled in an evidence-based intervention (4). PRISM provides a set of explanatory constructs that can act as contextual moderators or mechanistic mediators in the success of recruitment strategies intended to improve reach which include (1) the multi-level/multi-sector perceptions of a given intervention, (2) multi-leveled staff and setting characteristics, (3) the implementation and sustainability infrastructure of intervention delivery sites, and (4) external environmental factors (15). These constructs provide an opportunity to generate hypotheses to improve dissemination and implementation outcomes that can be used to characterize potential mechanisms (17). Of specific relevance to understanding the utility of strategies to improve reach, PRISM/RE-AIM includes hypotheses related to participant, delivery staff, organizational perceptions and characteristics, the implementation and sustainability infrastructure, characteristics of the intervention, and external factors that may mediate or moderate success (15). The primary objective of this review is to identify recruitment strategies to improve the reach (defined as number, proportion, and representativeness of eligible people) of lifestyle interventions focused on health promotion, disease prevention, and self-management for children and adults delivered in community or clinical settings.

We conducted a scoping review following the JBI Manual for Evidence Synthesis guidance (18). Using the framework as outlined by Arksey and O’Malley, we organized our scoping review into five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting the studies; (4) charting the data; and (5) collating, summarizing and reporting the results (19). As the JBI Manual for Evidence Synthesis states, “scoping reviews can be used to map the key concepts that underpin a field of research… the three most common reasons for conducting a scoping review [are] to explore the breadth or extent of the literature, map and summarize the evidence, and inform future research.” (20) For transparency and reproducibility, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) (21) and searches (PRISMA-S) (22) for reporting our literature search and review results. The protocol was registered on the Open Science Framework (osf.io) and is available at https://doi.org/10.17605/OSF.IO/3G68B. See Appendix E for differences between the protocol and manuscript.

We identified 9,712 references from our database search strategies. After removal of duplicates, 5,347 references were screened at title/abstract, then 208 references were assessed for eligibility through full-text review, and 98 studies from 100 references met our inclusion criteria (25–122). See Figure 1 for our PRISMA flowchart. No relevant studies were identified from checking the references of our included studies. Appendix C is a bibliography of our included studies. Appendix D is a bibliography of our excluded studies with reasons from the full-text screening.

The objective of this review was to identify recruitment strategies to improve the reach of lifestyle interventions focused on health promotion, disease prevention, and self-management for children and adults delivered in community or clinical settings. We also intended to report on the application of theory or conceptual models to improve the reach of lifestyle interventions across participants of all ages. We found that it appears as though place-based strategies achieve the highest participation rate, followed by point of care referrals, bundled strategies, and population health management. Of note, several strategies have only been used as part of recruitment bundles (e.g., engaging leaders to support recruitment or orientation events), making it difficult to determine the utility of these strategies.

In connection with this research topic area focused on aging, we documented that studies that focused on children recruited an average of 43% of the intended audience compared to 25% in adults and 16% for older adults when examined independently or with the full age range of adulthood. Compared to their younger counterparts, older adults may face unique barriers, such as deteriorating health and increasing social isolation, for participating in health promotion, disease prevention, and disease self-management interventions (126). In addition, older adults constitute a greater proportion of the population size in rural communities than urban communities, and older adults in rural communities can have fewer opportunities for such interventions (i.e., unavailability of the interventions) and greater transportation barriers to the interventions that may also influence reach (127).

However, it is likely premature to suggest that children are more likely to engage with lifestyle interventions; it may be more likely that there is a unique context of reach in these different age groups. For example, children and adolescents tend to be physically bound within school settings and have different types of social relationships that can influence recruitment (e.g., peers, teachers, and parents) compared to adults. Likely even more powerful is that recruitment through schools for school-based interventions has an inherently high reach (62). Similarly, a review of participation rates in workplace health promotion programs found an average participation rate just below 50%, which may be less related to the specific strategies and more related to having a known denominator in a setting where lifestyle interventions may be attractive as an employee benefit (128). Another review of enrollment of adults (18 years and older) with cancer and their caregivers in psychosocial or behavioral interventions trials (RCTs) resulted in an average enrollment rate of 33% (129). Both reviews observed participation rates that are higher than our participation rates ranging from 12 to 25% across strategies tested in more than 10 studies. These findings suggest the importance of considering the intended population and setting when planning reach strategies.

Our findings provide several directions for future research that examines the relative utility of different recruitment strategies to improve reach. First, there is a need to better define specific recruitment strategies and improve the application of theory or conceptual models to the design and application of strategies. Second, in addition to providing a recruitment strategy definition, dissemination science as it relates to reach would be better advanced by specifying strategies based on the strategy enactor, components, potential mechanism of improved reach, timing, dose, and intended reach outcome (6). Third, addressing the challenge of improving the reach of lifestyle interventions requires agreement on appropriate assessment of numerators, denominators, and characteristics of potential participants across temporal aspects of reach from exposure to recruitment activities, engagement in the enrollment process, enrollment itself, attendance at intervention sessions, and completion of the intervention (56). Fourth, despite the limitations of the current literature, participant characteristics such as age appear to be related to intervention reach, with higher rates of reach found for younger participants and lower rates with older adults.

Reach is a primary challenge across the spectrum of evidence-based lifestyle interventions whether during efficacy, effectiveness, or implementation trials (56, 87, 130–132). It is unsurprising that we identified 98 unique studies that evaluated or tested different recruitment strategies. We defined 15 unique strategies that were intended to improve intervention reach based on activities described to improve reach across studies (Table 2). However, we acknowledge that these definitions, while helpful, require additional scientific vetting due to potential overlap or muddling of recruitment setting and recruitment strategy. As the Expert Recommendations for Implementation Change compendium of strategies to improve implementation outcomes filled a gap in the implementation science literature (133), future work in this area should focus on addressing the gap in available and consistent labels and definitions for individual-level dissemination strategies that facilitate recruitment of those who would benefit from lifestyle interventions—and allow comparisons across populations, settings, and interventions.

While the field of dissemination and implementation science has a cornucopia of available theories, models, and frameworks, recent scoping reviews have suggested that there is an overemphasis on implementation and an underemphasis on dissemination (134). This may be surprising given the foundational work of Everett Rogers on the Diffusions of Innovation theory, which provides processes and mechanisms for both setting (i.e., adoption of innovations) and individual (i.e., reach of innovations within a population) level dissemination (135, 136). Still, our review supports the conclusion that there has been a lack of reporting the theory applied to the design, testing, and identification of mechanisms of change of strategies intended to improve reach. Indeed, only seven studies (25, 42, 47, 52, 60, 70, 118) referenced a theoretical approach and only five provided a description of potential mechanisms of change (25, 37, 50, 53, 85).

Understanding the underlying mechanisms or reasons why a specific strategy is successful allows for generalizability to other settings and provides guidance for recruitment strategy design in settings with different levels of resources to support recruitment. For example, population health management approaches may be designed to leverage the patient-provider relationship to improve potential participant normative beliefs of the benefits of enrolling in a lifestyle program (i.e., mechanism), which in turn leads to a higher participation rate. If one were to apply concepts from the Theory of Planned Behavior (137) to this example, qualitative and quantitative approaches could be used to see if leveraging the patient-provider relationship resulted in increased perceptions of subjective norm which lead to improved reach. Other authors have also highlighted the importance of understanding the mechanisms by which recruitment strategies can enhance participant engagement and adherence with, and to, evidence-based intervention components (138). Described as adjunctive interventions, Smith and colleagues suggest that methods targeting potential participants for health-focused interventions recruitment strategies can be designed to enhance motivation, self-efficacy, or capacity to engage with a health intervention (138).

In addition to underreporting of underlying theories and mechanisms to improve reach, we found that studies were highly variable in the degree to which they specified strategies based on the way, by who (including demographics and role), how often, and at what dose strategies were applied to improve reach. Important considerations for both the use and success of strategies to improve reach include the cost [reported by only 13 studies (25, 33, 41, 46, 48, 52, 80, 93, 94, 100, 103, 115, 119)], dose [reported by 50 (31, 36, 37, 39, 41, 44–50, 52–55, 58, 62, 64, 68–70, 73–76, 80, 82, 84–87, 91–94, 96, 98–100, 103–106, 108, 110, 111, 115, 117, 121)], and temporality [reported by 55 (25, 27, 30, 31, 34, 38, 39, 41, 42, 46–48, 50–54, 56, 57, 60–62, 64, 65, 68, 69, 71, 77, 80, 82, 85, 86, 88, 90–92, 94, 97–100, 102, 103, 106–108, 110–115, 117, 119, 122)]. Without this information, replication of strategies, and generalizing strategies into typical community or clinical settings is genuinely compromised.

Proctor and colleagues recommend specification of strategies based on the actor (i.e., who enacts the strategy), the action (i.e., the steps involved in the strategy), the factor or people intended to be influenced by the action (i.e., mechanism of change; priority population to be reached), temporality (i.e., when the strategy is used), dose (i.e., the duration of the strategy), the implementation outcome (i.e., fidelity or sustainment), and the justification (i.e., empirical or theoretical rational for using the strategy) (6). Previous reviews of literature using the RE-AIM framework provide direction on specifying factors related to the intended audience, intervention, and delivery settings (7, 139). For example, to improve reporting on characteristics of the intervention into which potential participants are being recruited, we recommend that the target intervention of the recruitment strategy also be specified in terms of the intervention delivery setting, staff, delivery mode and format, number, frequency and length of sessions. Similarly, we recommend defining the intended audience including subgroups that may experience health differences and the plan to assess reach for priority populations including the number, proportion, and representativeness to that priority population (140). In addition, studies that use community-engaged approaches to identify, engage, and enroll participants in lifestyle interventions typically provide details on the co-creation of the lifestyle intervention but do not describe, in detail, the engagement process in the selection and creation of recruitment strategies (23). We recommend that those using participatory approaches to develop recruitment strategies provide detail on the process of partnership development and involvement in recruitment strategy identification, creation, and deployment in addition to specifying the recruitment strategy factors (34).

It is of note that we used the reported participation rates from each included study. Unfortunately, the degree to which studies defined a denominator of those exposed to recruitment strategies varied widely, which makes conclusions around participation rate comparisons challenging. One of the key areas of inconsistency was around the operationalizing and reporting of a specific denominator based on the temporality of the recruitment process. Harden and colleagues highlighted this issue and proposed that proportional reach (i.e., participation rate) should be considered across four indicators (56). These were the proportion of the population (1) exposed to the recruitment strategy, (2) who respond to the recruitment strategy and express interest, (3) who enroll in the intervention, and (4) who attend intervention sessions over time. We would add to this characterization by adding a final temporal state of the proportion who complete the intervention and recommend that representativeness and tracking of priority population rates be assessed across each of these phases.

While our review excluded studies that focused only on recruitment strategies for clinical trials they may provide context to our recommendations given the potential similarities in recruitment barriers and facilitators. For example, our review included surprisingly few studies that applied financial incentives to support recruitment. In clinical trials, financial incentives to address social needs, transportation, and childcare have demonstrated success in recruiting under-represented populations (141). Similarly, strategies that ensure interventions are culturally relevant or have demographic concordance between the recruiter/implementer and participant have also been proposed to improve trust and recruitment into clinical trials (142). These findings may provide alternative methods to categorize strategies based on the underlying barrier or facilitator they are designed to address (e.g., trust, logistics). Still, differences between recruitment for clinical trials and lifestyle change intervention studies likely exist where, in one case, participants are asked to agree to testing a medication or providing biospecimens and, in the other, are asked to enroll in an intervention that promotes healthy behaviors. This raises questions about the characteristic of the innovation and underlying perceptions of the intended study population toward that innovation. These differences likely necessitate the application of different types, dose, and delivery channel of recruitment strategies—excellent areas of future research on strategies to promote lifestyle intervention reach. Finally, even within the variety of lifestyle change interventions it is likely that there are interactions between recruitment strategy type and the format of the intervention.

While this review sheds lights on an important gap in dissemination and implementation science related to strategies intended to improve lifestyle intervention reach, it is not without limitations. We provided labels, definitions, and categories of recruitment strategies for the studies we reviewed, but we anticipate that these labels and definitions are not exhaustive and present opportunities for further refinement. Additionally, although several databases were searched and references of our included studies were reviewed, published or unpublished studies might have been missed due to the vast number of lifestyle intervention studies conducted. We did not reach out to authors to address unreported data or for clarification. Since only eight studies used randomized controlled trials to test recruitment strategy influence on reach with little content overlap between these studies (26, 54, 60, 70, 86, 87, 103, 119), along with no assessment of quality or risk of bias conducted on included studies, the specific results should be interpreted with caution and used as a starting point rather than as a final destination for understanding participation rate. Although active learning was used in Covidence for screening, all studies were screened, selected and extracted in pairs by humans without artificial intelligence (AI) ensuring transparency and reproducibility.

In conclusion, the strengths of our review included a broad conceptualization of recruitment strategies and outcomes across 98 included studies to provide valuable information on available reach strategies, possible definitions, and a resource for others looking for research on improving reach. We analyzed studies to identify effective recruitment strategies for lifestyle interventions aimed at health promotion, disease prevention, and self-management. We found a variety of recruitment strategies, with 32% of studies using multiple strategies. Participation rates ranged from 3 to 41%, with higher rates observed in children (43%) compared to adults (25%) and older adults (16%). Most studies lacked detailed reporting on strategy timing, dose, theoretical basis, and mechanisms of improved reach, making comparisons difficult. The review highlights the need for consistent definitions of eligible participant denominators and the inclusion of theoretical approaches and mechanisms in future studies to enhance the understanding and effectiveness of recruitment strategies. Future research should focus on clearer reporting of recruitment strategies, including timing, dose, and theoretical underpinnings. Identifying and testing mechanisms that improve reach is crucial, as is considering the unique contexts of different age groups and settings. This will help develop more effective recruitment strategies that will improve enrollment for lifestyle interventions across all populations.