Department of Neurology, Xinxiang Central Hospital, The Fourth Affiliated Hospital of Xinxiang Medical College, Xinxiang, China.
Department of Anorectal Surgery, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Public Health. 2024 Apr 26;12:1395270. doi: 10.3389/fpubh.2024.1395270. eCollection 2024.
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 I 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: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=520100).
中风仍然是全球致残的主要原因。以护士为主导的电子健康(eHealth)计划已成为改善中风幸存者功能结局和生活质量的潜在有效策略。然而，由于试验之间的研究设计和测量结果存在差异，因此需要进行汇总分析以全面评估这些干预措施的疗效。本方案概述了一项汇总分析的方法学，旨在综合评估评估护士主导的电子健康干预措施对中风患者的随机对照试验(RCT)的证据。
本汇总分析将按照 PRISMA 指南进行。我们将纳入评估护士主导的电子健康计划并报告中风患者功能结局或生活质量的 RCT。将通过预先确定的搜索策略对包括 Pubmed、EMBASE、Cochrane 图书馆、CINAHL 和 PsycINFO 在内的电子数据库进行全面搜索。研究选择将包括筛选标题和摘要，然后使用明确的纳入和排除标准进行全文审查。数据提取将由两名评审员独立进行。通过 Cochrane 偏倚风险工具评估偏倚风险。此外，还将使用 GRADE 方法评估每个结局的证据质量。将使用随机效应模型进行荟萃分析，并使用 I 统计量量化异质性。亚组和敏感性分析将探索异质性的潜在来源。
本汇总分析有望通过使用彻底的方法学框架和 GRADE 工具，提供对护士主导的电子健康计划在中风康复中的有效性的细致理解，确保证据的稳健性和可靠性。该研究预计会改善患者结局，突显个性化、可及的电子健康干预措施增强患者参与度和治疗依从性的潜力。尽管干预措施和快速技术进步的异质性带来了挑战，但如果证据确凿，这些发现有望通过将电子健康纳入标准护理来影响临床路径。我们研究的深度和方法学严谨性有可能改变医疗保健政策，倡导采用电子健康并随后对其成本效益进行调查。最终，我们旨在为数字健康领域提供丰富、基于证据的见解，为电子健康在中风护理中的应用提供基础评估。我们的数据预计将产生持久影响，不仅指导当前的临床决策，还塑造中风康复未来医疗保健策略的轨迹。
标识符（CRD42024520100：https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=520100）。
