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

Abstract

Background: Stroke remains a major global health burden, with high recurrence rates despite preventability through standardized interventions. Mobile health (mHealth) interventions show promise in stroke recurrence prevention, yet mHealth implementation varies significantly across different resource settings. This study aimed to investigate implementation determinants and strategy alignment patterns in mHealth interventions for recurrent stroke prevention between low and high resource settings.

Methods: Six databases [PubMed, Web of Science, Cochrane Library, Scopus, CNKI (China National Knowledge Infrastructure), and Wanfangdata] were searched for the publication period from January 2013 to December 2023. We included empirical studies and evidence syntheses of mHealth interventions for secondary stroke prevention with implementation descriptions, excluding those using specialized medical devices, robot-assisted interventions, or involving participants with significant comorbidities. Implementation determinants were coded using the Consolidated Framework for Implementation Research (CFIR) constructs, and implementation strategies were mapped using Expert Recommendations for Implementing Change (ERIC) taxonomy. Strategy-barrier alignment was summarized by comparing implemented versus expert-recommended strategies across settings. Statistical significance was assessed using non-parametric tests and bootstrap analyses, with sensitivity analyses accounting for study quality.

Results: Fifty-five studies were included, with 52.7% conducted in low resource settings. 74.5% were published between 2019-2023, with randomized controlled trials (RCTs) being the most common study design (49.1%). Interventions primarily utilized smartphone applications (APPs) (49.1%) and instant messaging systems (IMS) (25.5%). Key CFIR determinants differed between resource settings. "Relative Advantage" (9/29 vs. 4/23) and "Access to knowledge & information" (11/29 vs. 5/23) were emphasized in low resource settings, while "Design Quality & Packaging" (2/29 vs. 9/23) and "Reflecting & Evaluating" (1/29 vs. 6/23) were highlighted in high resource settings. There was a higher adoption of recommended strategies in low resource settings compared to high resource settings (9.40 vs. 7.16 matches per study) as well as more gaps in reported strategies (9.53 vs. 8.00 gaps per study). Mann-Whitney U tests showed marginally significant differences in strategy adoption, with bootstrap analysis confirming it [mean difference =2.20, 95% confidence interval (CI): 0.36-4.12]. Implementation gaps showed no significant difference between settings (P=0.34).

Conclusions: Implementation determinants and strategy adoption vary between low and high resource settings. Low-resource settings demonstrate significantly greater adoption of ERIC strategies. Context-tailored policies are critical to bridge know-do gaps in implementing stroke prevention intervention globally.