Stroke remains a leading cause of disability worldwide. Nurse-led eHealth programs have emerged as a potentially effective strategy to improve functional outcomes and quality of life in stroke survivors. However, the variability of study designs and outcomes measured across trials necessitates a pooled analysis to comprehensively assess the efficacy of these interventions. This protocol outlines the methodology for a pooled analysis that aims to synthesize evidence from randomized controlled trials (RCTs) evaluating nurse-led eHealth interventions for stroke patients.
This pooled analysis will be conducted according to the PRISMA guidelines. We will include RCTs that evaluate nurse-led eHealth programs and report on functional outcomes or quality of life in stroke patients. Comprehensive searches of electronic databases including Pubmed, EMBASE, the Cochrane Library, CINAHL, and PsycINFO will be conducted with a predefined search strategy. Study selection will involve screening titles and abstracts, followed by full-text review using explicit inclusion and exclusion criteria. Data extraction will be undertaken independently by two reviewers. The risk of bias will be assessed through the Cochrane Risk of Bias tool. Additionally, the quality of evidence for each outcome will be evaluated using the GRADE approach. Meta-analyses will be performed using random-effects models, and heterogeneity will be quantified using the I2 statistic. Subgroup and sensitivity analyses will explore potential sources of heterogeneity.
This pooled analysis is poised to provide a nuanced understanding of the effectiveness of nurse-led eHealth programs in stroke rehabilitation, leveraging a thorough methodological framework and GRADE tool to ensure robustness and reliability of evidence. The investigation anticipates diverse improvements in patient outcomes, underscoring the potential of personalized, accessible eHealth interventions to enhance patient engagement and treatment adherence. Despite the challenges posed by the heterogeneity of interventions and rapid technological advancements, the findings stand to influence clinical pathways by integrating eHealth into standard care, if substantiated by the evidence. Our study’s depth and methodological rigor possess the potential to initiate changes in healthcare policy, advocating for the adoption of eHealth and subsequent investigations into its cost-efficiency. Ultimately, we aim to contribute rich, evidence-based insights into the burgeoning field of digital health, offering a foundational assessment of its applications in stroke care. Our data is expected to have a lasting impact, not only guiding immediate clinical decisions but also shaping the trajectory of future healthcare strategies in stroke recovery.
Identifier (CRD42024520100: