A scoping review of implementation determinants and strategy alignment patterns in mHealth interventions for stroke recurrence prevention between low and high resource settings

Introduction

Background

Stroke ranks as the primary contributor to death and disability worldwide, regardless of resource settings (1), with recurrence further elevating the risks of adverse health outcomes (2-4). While stroke is largely preventable through standardized treatment and risk factor control (1,5,6), real-world implementation of evidence-based prevention strategies remains suboptimal, particularly in resource-limited settings.

The suboptimal implementation of intervention strategies manifests in two critical areas. First, clinical practice guidelines, despite their proven effectiveness, often encounter limited active dissemination and low implementation rates in routine care (7,8). Second, ensuring patient adherence to recommended post-discharge regimens presents persistent challenges, especially in maintaining long-term lifestyle modifications and medication compliance (9,10). These gaps in initiating timely intervention and maintaining self-care measures increase risk of recurrent events (11-13) which carry substantially higher mortality rates and worse outcomes.

Mobile health (mHealth) interventions have emerged as a promising solution to address these implementation challenges (14,15). mHealth offers several advantages for stroke prevention utilizing widespread mobile phone penetration: it enables real-time patient-provider interaction and timely feedback (16,17), facilitates early risk identification and symptom management (18,19), and supports sustained patient engagement in self-management (20). Systematic evidence confirms that mHealth interventions enable effective self-management support for stroke survivors through pharmaceutical interventions, public health services and social measures supported by mobile devices with relative high acceptance (21), offering a lightweight (1,22), cost-effective alternative to traditional care, particularly suited for resource-limited settings (23,24).

Rationale and knowledge gap

Recent empirical studies demonstrate varying success in translating mHealth interventions into practice. Trials (25-28) have shown improved clinical outcomes in low resource settings, meta-analyses (16,29) and reviews (30,31) confirmed effectiveness in chronic disease management, yet translating this potential into sustainable implementation faces distinct challenges across resource settings. Resource-rich countries primarily grapple with integration into existing healthcare systems and workflow adaptation, while resource-limited countries often confront more fundamental barriers in technological infrastructure and healthcare workforce capacity. Implementation research suggests high satisfaction and acceptability of mHealth interventions among stroke survivors (17,32), particularly regarding home-based therapy convenience (10). However, implementation gaps remain underexplored, notably pronounced in the identification of context-specific determinants and strategy adoption patterns making it difficult to develop targeted strategies that can effectively bridge the know-do gap in stroke prevention across settings (33-37).

Implementation science frameworks offer valuable theoretical guidance for analyzing these complex implementation dynamics. The CFIR (Consolidated Framework for Implementation Research) provides a theoretical foundation for analyzing determinants in implementation, particularly relevant for mHealth interventions where technological innovation intersects with varying healthcare system capacities. The ERIC (Expert Recommendations for Implementing Change) complements this by offering a taxonomy of evidence-based implementation strategies (38-41). While the updated version of CFIR [2022] (42,43) focuses on user feedback rather than theoretical advances, the initial version [2009] (38) better analyzes strategy-barrier alignment when paired with ERIC (39,41)—a critical but understudied aspect of mHealth implementation (44).

Despite growing evidence supporting mHealth interventions’ effectiveness in stroke prevention, three key knowledge gaps persist. First, there lacks systematic understanding of implementation determinants in different resource settings. Second, the alignment between implementation strategies and contextual barriers remains poorly documented, particularly in comparing resource-rich versus resource-limited contexts. Third, evidence-based guidance for selecting and tailoring context-appropriate implementation strategies remains limited.

Objective

This study aims to address these gaps by: (I) identify implementation determinants across different income-specific contexts using CFIR; (II) evaluate strategy-barrier alignment through CFIR-ERIC mapping; and (III) propose context-specific recommendations for selecting and adapting implementation strategies. By systematically analyzing implementation patterns across resource settings, this study seeks to advance understanding of how to effectively translate mHealth interventions for stroke prevention into sustainable real-world practice. We present this article in accordance with the PRISMA-ScR reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-20/rc) (45).

Methods

Study design and theoretical framework

Our theoretical approach integrated two complementary frameworks: CFIR provided a comprehensive lens for identifying implementation determinants, while ERIC taxonomy offered a structured approach for mapping implementation strategies. We selected the 2009 CFIR version for three methodological reasons: (I) it has been extensively validated in implementation research with over a decade of empirical application; (II) the CFIR-ERIC Barrier Buster Tool (v0.53)¹, which was essential for our strategy-barrier alignment analysis, was developed and validated specifically for the 2009 framework constructs; and (III) no comparable validated mapping tool currently exists for the 2022 CFIR version. While the 2022 update offers valuable user-centered refinements for digital health, it mainly refines definitions rather than structure. For systematically mapping barriers to strategies, the 2009 version with its validated CFIR-ERIC Barrier Buster Tool provided the most rigorous approach. This dual-framework approach enabled systematic examination of strategy-barrier alignment patterns across different resource settings.

Information sources and search strategy

Literature search was conducted in six databases: English databases included PubMed, Web of Science, the Cochrane Library, and Scopus; Chinese databases included CNKI (China National Knowledge Infrastructure) and Wanfangdata (WanFang Digital Database). The search was limited to articles published between 1 January 2013 and 31 December 2023 (search conducted on 5 January 2024). The search strategy combined four concept blocks using Medical Subject Headings (MeSH) terms and keywords: (I) stroke; (II) recurrence, recurrent or secondary prevention; (III) mHealth interventions; and (IV) implementation factors (determinants, barriers, facilitators, etc.). All retrieval processes were independently conducted by two reviewers (J.P. and H.T.), the final search results were exported to EndNote V.21 and duplicates were removed. Detailed search terms can be found in Appendix 1. Additional relevant studies were identified through manual searching of reference lists and citation tracking of included studies.

Eligibility criteria

Studies were eligible if they: (I) examined mHealth interventions for stroke prevention or management; (II) contained implementation descriptions; (III) were conducted in any resource setting as classified by Organization for Economic Co-operation and Development (OECD) criteria (46); and (IV) used any empirical study design [e.g., randomized controlled trials (RCTs), cohort studies, qualitative studies] or synthesized empirical evidence (e.g., meta-analyses). We excluded studies utilizing specialized medical devices (e.g., electromyographic biofeedback, physiological data acquisition systems, real-time visual feedback systems), robot-assisted interventions, or those involving participants with significant comorbidities (obstructive sleep apnea, cognitive impairment, severe psychiatric disorders, refractory epilepsy). Additionally, we excluded conference abstracts, letters, and studies conducted exclusively in acute care settings. Both English and Chinese language publications were considered.

Study selection and data extraction

The same pair of independent reviewers screened the titles/abstracts and full texts against eligibility criteria, with disagreements resolved through discussion or third-reviewer adjudication (Xiru Yu). A data charting form was jointly designed by the investigator team a priori to extract: study information (title, first author, year of publication, country/region, study design, etc.), mHealth intervention and control group details, CFIR-categorized implementation determinants (enablers or barriers) (38,43,47), quality assessment results.

CFIR-ERIC mapping

First, two reviewers independently coded implementation barriers according to CFIR constructs, reaching consensus through discussion. Second, implementation strategies were extracted using the ERIC taxonomy. Third, CFIR-categorized barriers were mapped to expert-recommended strategies using the CFIR-ERIC Barrier Buster Tool V0.53, identifying Level 1 strategies (endorsed by >50% experts) for each barrier (40). Finally, we summarized strategy-barrier alignment by calculating “matches per study” (reported Level 1 strategies in implementation) and “missing per study” (non-reported Level 1 strategies in implementation) rates, comparing patterns between low and high settings. Coding agreement analysis for CFIR coding was done (Appendix 2).

Quality assessment

We adapted the Standard Quality Assessment Criteria for Evaluating Primary Research Papers (48), rather than use implementation science specific quality criteria, as guide to provide an overview of the quality of studies with a validated 9-item checklist applicable across various research fields. Following previous implementation research approaches (49), we applied this assessment framework to both quantitative and qualitative studies (48,49) (Appendix 3). Each item was scored on a 3-point scale (0–2), with items marked “N/A” excluded from scoring. Final quality scores were calculated as the ratio of actual to possible points, categorizing studies as low (0–0.61), medium (0.67–0.79), or high quality (0.83–1.00). We note that while implementation science has specific reporting guidelines, such as Template for Intervention Description and Replication (TIDieR) and Standards for Reporting Implementation Studies (StaRI) Statement, which could provide additional insights into implementation quality, our quality assessment focused on evaluating the methodological rigor across different study designs. Two reviewers conducted assessments independently, resolving discrepancies through discussion or consultation with a third reviewer.

Data synthesis

We employed a convergent integrated approach to synthesize the quantitative and qualitative implementation evidence. Descriptive data from included studies were charted in Microsoft Excel 2019. Findings were organized according to: (I) study and intervention characteristics; (II) CFIR-categorized implementation determinants; (III) ERIC-classified implementation strategies; and (IV) strategy-barrier alignment patterns for resource-rich and resource-limited contexts. The frequency of CFIR constructs and ERIC strategies were summarized, while narrative synthesis explored patterns across resource settings. The synthesis emphasized actionable insights for selecting and adapting implementation strategies across contexts.

Statistical analysis

We conducted statistical analyses to examine differences in strategy alignment patterns between resource settings. First, we assessed data normality using Kolmogorov-Smirnov and Shapiro-Wilk tests. For non-normally distributed data, we employed Mann-Whitney U tests with exact significance calculations. Additionally, given the small sample sizes in each group (n<30), we employed bootstrap methods to obtain robust estimates of means, standard deviations, and confidence intervals for strategy alignment patterns. Bias-corrected and accelerated (BCa) 95% confidence intervals (CIs) were calculated for mean differences between resource settings. Statistical significance was assessed using bootstrap-derived P values. All analyses were performed using SPSS version 26.0.

Results

Literature search and study selection

The initial search identified 783 articles, of which 92 were duplicates and 574 were removed based on titles and abstracts. Full-text screening was conducted on 117 studies, of which 19 could not obtain the full text, 24 did not meet our criteria for mHealth interventions, and 40 were not related to stroke recurrence. In the additional manual search, 138 articles were retrieved, including 29 duplicates, 52 excluded by title or abstract, 11 could not obtain the full text, 8 did not align with the definition of mHealth interventions, and 17 were unrelated to stroke recurrence. Ultimately, 55 articles were included (Figure 1).

Figure 1 PRISMA flow diagram for included papers. PRISMA, Preferred reporting items for Systematic Review and Meta-Analyses.

Characteristics of included studies

Publication trends and study design

Among the included 55 studies spanning 2013–2023 (Table 1), 74.5% published since 2019, indicating growing research interest in this field. RCTs constituted the majority (49.1%) of study designs, followed by qualitative studies (11.8%), pilot studies (10.1%), and observational studies (10.1%). Forty-seven used quantitative research checklists, 5 qualitative, and 3 both. Quality assessment suggested high quality in 43.6% of studies, with 23.6% rated as low quality, reflecting varied methodological rigor across the literature. This distribution reflects an emphasis on generating high-quality evidence while maintaining methodological diversity.

Geographical distribution and implementation settings

Studies originated from diverse geographical locations, with China (45.5%) and the United States of America (USA) (16.9%) contributing the largest proportions. The distribution between low and high resource settings was nearly balanced, with 29 studies (52.7%) conducted in low resource settings, enabling meaningful cross-context comparisons. This geographical spread provides valuable insights into implementation variations across different healthcare systems and resource settings.

mHealth intervention characteristics

Technology platforms and features

The interventions utilized various technological platforms, including smartphone applications (APPs) (49.1%), instant messaging systems (IMS) (25.5%), web portals (14.5%), personal social media (14.5%), video-teleconferencing (5.5%), and work-oriented social media (3.6%). Many interventions integrated multiple platforms to enhance functionality and accessibility. These platforms were typically enhanced with features such as personalized lifestyle modification guidance, family engagement modules, real-time healthcare provider interactions, remote monitoring capabilities through Bluetooth devices, integrated condition-specific management tools, etc.

Implementation determinants analysis

CFIR framework application

CFIR constructs frequency suggested distinct patterns in implementation determinants across settings (Table 2). Of the included 55 studies, “Evidence strength & Quality” (mentioned in 32 studies), “Adaptability” (27 studies), and “Complexity” (24 studies) emerged as predominant concerns among Intervention Characteristics. In the Outer Setting, “Patient Needs & Resources” was most frequently mentioned (39 studies). The Inner Setting suggested “Implementation Climate” (24 studies) and “Available Resources” (22 studies) as key factors. For Individual Characteristics, “Knowledge & Beliefs about the Intervention” (6 studies) and “Self-efficacy” (5 studies) had relatively higher frequency. Within Process dimensions, “Executing” (10 studies) and “Reflecting & Evaluating” (9 studies) were commonly cited. Summary of findings on barriers and enablers using CFIR are elaborated in the Appendix 4. Full records of barriers identified are documented in table online: https://cdn.amegroups.cn/static/public/mhealth-25-20-1.xlsx.

Cross-context comparison

Comparison of included studies (n=52, excluding meta-analyses due to their inability to be geographically categorized for resource setting classification) across CFIR dimensions suggested distinct patterns between resource-rich (n=23) and resource-limited contexts (n=29). Within Intervention Characteristics, both frequently reported “Evidence Strength & Quality” (16/29 vs. 13/23), “Adaptability” (16/29 vs. 9/23), and “Complexity” (16/29 vs. 8/23), while differences emerged in “Relative Advantage” (9/29 vs. 4/23) and “Design Quality & Packaging” (2/29 vs. 9/23). In the Outer Setting, “Patient Needs & Resources” showed consistently high frequency (20/29 vs. 16/23). The Inner Setting suggested similar emphasis on “Implementation Climate” (13/29 vs. 10/23) and “Available Resources” (11/29 vs. 10/23), but differences in “Access to knowledge & information” (11/29 vs. 5/23) and “Networks & Communications” (4/29 vs. 0/23). For Individual Characteristics, “Self-efficacy” received universal attention (3/29 vs. 2/23), yet differentiated in “Knowledge & Beliefs about the Intervention” (5/29 vs. 10/23). In the Process dimension, “Executing” were prioritized comparably between settings (6/29 vs. 4/23), while diverging on “Reflecting & Evaluating” (1/29 vs. 6/23).

Strategy-barrier alignment

Implementation strategy recommendations

Identified CFIR implementation determinants and the expert-endorsed ERIC strategies mapped for 55 included studies were organized in table online: https://cdn.amegroups.cn/static/public/mhealth-25-20-1.xlsx. Table 3 illustrates the most frequently recommended ERIC strategies among Level 1 strategies, assessment-focused approaches were predominantly endorsed, with “Assess for readiness and identify barriers and facilitators” recommended in 94.5% of studies. Leadership engagement strategies were also prioritized, as evidenced by “Identify and prepare champions” being recommended in 92.7% of studies. Knowledge-sharing and stakeholder engagement strategies followed closely, with both “Capture and share local knowledge” and “Involve patients/consumers and family members” endorsed in 87.3% of studies. Consensus-building and educational strategies also featured prominently, with “Conduct educational meetings”, “Conduct local consensus discussions”, and “Identify early adopters” each recommended in 85.5% of studies. Additional frequently recommended strategies included “Creating a learning collaborative” and “Tailoring strategies” (83.6% each), followed by “Promoting adaptability” and “Conducting local needs assessment” (81.8% each). Strategies also focused on iterative improvement and stakeholder feedback, such as “Conduct cyclical small tests of change” and “Obtain patient/family feedback”, were recommended in 78.2% of studies.

For Level 2 strategies, “Develop academic partnerships” emerged as the most frequently recommended approach (60.0%), followed by “Intervene with patients/consumers to enhance uptake & adherence” (50.9%). Knowledge transfer strategies, including “Shadow other experts” and “Work with educational institutions”, were each recommended in 47.1% of studies. Clinical data management and quality assurance strategies, such as “Facilitate relay of clinical data to providers” and “Audit and provide feedback”, were endorsed in 45.5% of studies. Financial considerations were addressed through strategies like “Alter patient/consumer fees” (49.1%) and “Use other payment schemes” (41.8%). Organizational support mechanisms, including “Involve executive boards” (47.3%) and “Develop and organize quality monitoring systems” (38.2%), were also prominently featured.

Strategy alignment patterns

When comparing CFIR-ERIC tool-recommended Level 1 strategies with reported implementation approaches, we observed variations in strategy adoption across settings (Table 4). Studies conducted in low resource settings (n=29) demonstrated a higher average number of strategies matches with Level 1 strategies (9.40±4.31 matches per study) compared to those conducted in high resource settings (n=23, 7.16±2.44 matches per study). However, studies conducted in low resource settings also showed more gaps between the recommended strategies and the reported strategies (9.53±5.49 versus 8.00±4.63 per study), suggesting distinct patterns in strategy selection and implementation approaches across resource settings.

To evaluate the statistical significance of these differences, we followed a systematic analytical approach (Tables 5,6). Normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) indicated non-normal distributions (P<0.05), leading us to employ Mann-Whitney U tests with exact significance. For matches per study, the test revealed a marginally significant difference (U=243.0, exact two-tailed P=0.09, exact one-tailed P=0.047). Bootstrap analyses (5,000 samples) provided robust confirmation and precise estimates of the effect size: the mean difference was 2.20 (BCa 95% CI: 0.36–4.12), with a bootstrap-derived P value of 0.03, indicating low-resource settings implemented on average 2.2 more recommended strategies per study. For missing strategies, the mean difference of 0.57 (BCa 95% CI: −2.29 to 3.38) suggested no meaningful difference between settings. Complete statistical outputs are provided in Appendix 5.

Sensitivity analysis: quality-weighted strategy alignment

The quality-weighted analysis assigned greater weight to higher-quality studies while retaining all data. For matches per study, the difference between low-resource (weighted mean =9.32, 95% CI: 8.75–9.91) and high-resource settings (weighted mean =7.31, 95% CI: 6.95–7.66) remained statistically significant (weighted mean difference =2.01, 95% CI: 1.35–2.69, P<0.001). For missing strategies, no significant difference was found between low-resource settings (mean: 9.21, 95% CI: 8.51–9.89) and high-resource settings (mean: 8.59, 95% CI: 7.96–9.25), consistent with our primary analysis. Refer to Appendix 5 for details.

Discussion

Key findings

This study suggested three core findings about mHealth implementation for stroke recurrence prevention: (I) implementation determinants between low and high resource settings; (II) differential strategy-barrier alignment patterns between resource-rich and resource-limited contexts; and (III) varying context-specific implementation priorities across settings.

First, distinctive implementation patterns were uncovered between low and high resource settings across multiple CFIR domains. While resource-limited contexts emphasized demonstrating relative advantage, ensuring access to knowledge and information access, and forging networks, resource-rich contexts focused more on intervention refinement through design quality and systematic evaluation.

Second, examination of ERIC strategy recommendations suggested consistent patterns across implementation settings. Assessment-focused approaches were predominantly endorsed, followed by leadership engagement through shaping champions. Knowledge-sharing and stakeholder engagement strategies were also considered, suggesting broad expert consensus on fundamental implementation strategies regardless of resource setting.

Third, examination of strategy-barrier alignment studies revealed complex patterns between settings through multiple statistical approaches. While Mann-Whitney U tests showed marginally significant differences in strategy adoption (exact P=0.09, one-tailed P=0.047), bootstrap analyses provided more robust evidence of significant differences (mean difference =2.20, bootstrap BCa 95% CI: 0.36–4.12, P=0.03). This convergent evidence indicates that studies in low-resource settings implemented on average 2.2 more recommended strategies per study. Conversely, gaps between recommended and reported strategies showed no significant differences across both analytical approaches (Mann-Whitney U P=0.34; bootstrap BCa 95% CI: −2.29 to 3.38), suggesting that implementation challenges persist regardless of resource availability. Importantly, these patterns remained robust in sensitivity analyses that accounted for study quality.

Strengths and limitations

This study advances implementation science in three aspects. First, by systematically mapping implementation determinants through CFIR and connecting them to recommended strategies via ERIC taxonomy, we provide insights into both the distribution of specific barriers and their corresponding evidence-based solutions across settings, moving beyond simple barrier identification to actionable implementation guidance. Second, incorporating bilingual literatures enhances the findings’ representatives to some degree, especially in understanding implementation dynamics in emerging economies. Third, strategy-barrier alignment patterns offer practical guidance for choosing and tuning implementation approaches considering local conditions, enabling policymakers and program implementers to prioritize context-appropriate strategies that address the most pressing barriers in their specific resource environment. Fourth, our multi-faceted statistical approach strengthens the validity of our findings. By employing both non-parametric tests appropriate for non-normal distributions and bootstrap methods for robust estimation, we provide convergent evidence for our key findings.

Several limitations warrant consideration. First, sparse implementation details in primary studies and language constraints reflect broader gaps between knowledge translation and implementation research, where fragmented evidence ecosystems delay real-world impact (103,104). Second, our strategy-barrier alignment analysis relied exclusively on the information reported by authors, so we cannot determine whether observed gaps reflect actual implementation deficits or underreporting. Third, while our use of the 2009 CFIR version enabled validated strategy-barrier mapping, it may not fully capture user-centered dynamics particularly relevant to mHealth. The 2022 update’s refinements could offer additional insights into mHealth adoption patterns. Fourth, our review lacks longitudinal data and causal mechanism analyses necessary to establish long-term effectiveness of identified strategies. Finally, our search cutoff of December 2023 may not capture recent advances in mHealth implementation, particularly given the rapid evolution of digital health technologies accelerated by the coronavirus disease 2019 (COVID-19) pandemic. This temporal limitation means emerging post-pandemic implementation strategies may not be reflected in our findings, highlighting the need for regular systematic updates in this rapidly evolving field.

Comparison with similar research

Our findings resonate with literatures emphasizing the need for embedded approaches in mHealth implementation (103,104), where policymakers, technologists, and end-users collaboratively design interventions to address localized barriers, such as family dynamics paradoxically reducing mHealth willingness when communication is strong (64,105).

Current preventive strategies for stroke recurrence have gained critical priority in public health agendas, driven by the escalating global burden of stroke and its recurrent events (29). While existing literature positions mHealth as a cost-effective (106) and accessible (1) solution under resource constraints, our findings extend this understanding by summarizing the distinct patterns of implementation determinants and strategy adoption that emerge in different resource contexts, thereby providing a more nuanced framework for context-specific implementation planning.

Explanations of findings

The observed patterns in implementation determinants and strategy adoption may be explained by several factors. The divergent focus between low and high resource settings reflects their different stages of implementation maturity and resource availability. Low resource settings’ emphasis on demonstrating intervention effectiveness and building fundamental capacity aligns with their need to establish basic implementation infrastructure, consistent with findings by Asige et al. who emphasized that rigorous effectiveness evaluation in real-world situations is a prerequisite before scaling up complex interventions in resource-constrained settings (107). High resource settings’ focus on intervention refinement and systematic evaluation reflects their more developed healthcare systems and existing digital infrastructure, as demonstrated by Ross et al., who found that implementation in high-income countries often centers on interoperability with existing systems rather than fundamental capacity building (108). This also aligns with Greenhalgh et al.’s work showing how technology adoption in mature healthcare systems frequently emphasizes optimization over initial functionality (109).

The convergent statistical evidence for higher strategy adoption in resource-limited contexts warrants careful interpretation. The marginally significant Mann-Whitney U test (P=0.09) gained stronger support through bootstrap analysis (P=0.03). This pattern indicates that countries with limited resources may indeed be more motivated to adopt evidence-based strategies, a phenomenon documented by Iwelunmor et al. in their systematic review of implementation science in regions with restricted resources (110). The consistency between our primary and sensitivity analyses further strengthens this interpretation, as the quality-weighted analysis yielded similar effect sizes (2.01 vs. 2.20 strategies), indicating that higher-quality studies support this pattern. Meanwhile, the non-significant differences in implementation gaps across all analytical approaches (P=0.34 in primary analysis, P=0.22 in sensitivity analysis) suggest that structural barriers to comprehensive implementation transcend resource availability, aligning with Moucheraud et al., who found that limited-resource settings often prioritize a subset of recommended strategies based on feasibility rather than attempting comprehensive implementation (111). The pattern also highlights the need for context-sensitive approach tailoring and implementation planning that considers both local implementation capacity and resource constraints, supporting Aarons et al.’s framework for adapting evidence-based practices across different service settings (112) and Shelton et al.’s findings on the importance of contextual fit in implementation success (113).

Implications and actions needed

Research implications

Future research should examine optimal strategy combinations and evaluate implementation effectiveness across different resource settings, with special focus on longitudinal, mixed-methods designs to disentangle causal mechanisms. Expanding multilingual evidence synthesis may also help understand implementation dynamics more comprehensively across diverse contexts. Building on our CFIR-ERIC mapping findings, future studies may further develop and validate context-specific implementation toolkits that provide actionable guidance for specific resource environments.

Practice implications

For practice, our findings suggest that implementation strategies should be tailored to local healthcare system maturity. Resource-limited countries tend to focus on scaling basic mobile platforms while building workforce capacity, whereas resource-rich countries typically prioritize integration with existing digital infrastructure. Future implementation efforts may benefit from considering these contextual patterns, though more research is needed to establish whether these approaches lead to optimal outcomes in their respective settings. Implementers in resource-limited settings might particularly benefit from focusing on the commonly underutilized but expert-recommended strategies identified in this study.

Policy implications

Policymakers should consider differential approaches to supporting mHealth implementation based on healthcare system context. In resource-limited settings, policies that support basic technological infrastructure development and healthcare workforce capacity building may create fertile ground for mHealth adoption. Conversely, in resource-rich settings, policies that facilitate interoperability standards and integration with existing health information systems may better address identified implementation barriers. Cross-cutting policy considerations include developing context-sensitive funding mechanisms that account for the different implementation priorities highlighted in this study and other researches.

Conclusions

This study reveals key disparities in mHealth implementation for stroke prevention between low- and high-resource settings. Low resource contexts prioritize foundational capacity (demonstrating intervention effectiveness, knowledge access), while high resource settings focus on refining interventions (design quality, evaluation). Our statistical analyses confirm that low resource settings demonstrate significantly higher adoption of expert-recommended strategies, yet face comparable implementation gaps, revealing a paradox between implementation ambition and realization. CFIR-ERIC mapping identifies universal adoption of assessment-focused and stakeholder engagement strategies, yet low resource settings exhibit higher strategy adoption alongside persistent gaps, potentially reflecting ambitious implementation efforts constrained by realistic limitations. These findings provide a structured framework for context-aware implementation, informing resource-sensitive strategies. Future research should explore causal mechanisms of strategy effectiveness and longitudinal impacts across diverse systems, particularly to understand why greater strategy adoption in resource-limited settings does not translate into fewer implementation gaps. To bridge know-do gaps, policymakers should develop tailored approaches that acknowledge these contextual differences, thereby enhancing the translation of effective mHealth interventions into sustainable real-world practice in secondary prevention of stroke globally.

Acknowledgments

None.

Footnote

Downloaded from https://cfirguide.org/choosing-strategies/.

Reporting Checklist: The authors have completed the PRISMA-ScR reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-20/rc

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-20/prf

Funding: This study was funded by the Ministry of Science and Technology of the People’s Republic of China - National Key R&D Program of China (No. 2022YFC3603000).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-20/coif). Y.M. and Xiaoling Yan report that the study was funded by the Ministry of Science and Technology of the People’s Republic of China - National Key R&D Program of China (No. 2022YFC3603000). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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