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Description: Abstract: Background: Hypertension is a major risk factor for stroke recurrence in stroke patients. Home blood pressure monitoring, facilitated by digital health technologies and led by nurses, may improve blood pressure control in this high-risk population. However, the evidence is not yet conclusive. This study protocol outlines a pooled analysis of the current literatures to evaluate the effectiveness of nurse-led digital health programs for home blood pressure monitoring in stroke patients. Methods and Analysis: We will conduct a comprehensive search of some major electronic databases (e.g., PubMed, EMBASE, Cochrane Library, and CINAHL) and trial registries for randomized controlled trials evaluating nurse-led digital health programs for home blood pressure monitoring in stroke patients. Two reviewers will independently screen titles and abstracts, review full-text articles, extract data, and assess risk of bias using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2.0). The primary outcome will be the change in systolic blood pressure. Secondary outcomes will include diastolic blood pressure, adherence to the program, patient satisfaction, and stroke recurrence. Data will be pooled and analyzed using meta-analysis techniques, if appropriate. Discussion: This study will provide comprehensive evidence on the effectiveness of nurse-led digital health programs for home blood pressure monitoring in stroke patients. The findings could have substantial implications for clinical practice and health policy, potentially informing the development of guidelines and policies related to hypertension management and stroke prevention. Conclusions: By pooling the results of randomized controlled trials, this study will offer a robust evidence base to inform clinical practice and health policy in the context of stroke patients. Despite potential limitations such as heterogeneity among studies and risk of publication bias, the rigorous methodology and comprehensive approach to data synthesis will ensure the reliability and validity of the findings. The results will be disseminated through a peer-reviewed publication and potentially at relevant conferences. Study Design The study will employ a pooled analysis of RCTs based on both the study level data and level of individual participant data (IPD) whatever data are available, which are considered the optimal approach to minimize confounding factors and bias. Specifically, this analysis will focus on RCTs that have investigated the effects of nurse-led digital health programs on home blood pressure monitoring in patients with a history of stroke. The pooling of data in this context will not only be at the individual participant data (IPD) level but also at the aggregate data level. This means that summary statistics from each study, along with the raw patient data, will be combined to enhance statistical power and improve the precision of the estimated effects of the intervention. This approach will allow us to integrate findings from multiple studies, providing a more robust and generalizable understanding of the nurse-led interventions' impact on blood pressure control among stroke survivors. Each selected RCT will be carefully assessed for compatibility in terms of intervention components, patient populations, and outcomes measured to ensure validity in the synthesis of the data. Data Sources The search for studies to include in the pooled analysis will be a systematic process involving multiple electronic databases. MEDLINE (via PubMed), EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), and CINAHL will be searched as they cover a broad range of biomedical literature, including nursing and allied health literature. A health sciences librarian will be consulted to develop a robust search strategy, which will include MeSH terms and free-text terms related to stroke, hypertension, home blood pressure monitoring, digital health, and nurse-led interventions (Table 1). Additionally, the reference lists of the included studies and relevant systematic reviews will be examined for potential studies. Clinical trial registries like ClinicalTrials.gov and WHO International Clinical Trials Registry Platform will also be searched for ongoing and completed trials. Eligibility Criteria The selection of studies for the pooled analysis will be based on the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework. The inclusion criteria are as follows: Population: Adult patients (aged 18 years or older) with a history of stroke. Intervention: The intervention under review is a nurse-led digital health program for home blood pressure monitoring (HBPM). This program is designed to empower patients with a history of stroke to manage their blood pressure at home, with nurses playing a key role in the process. The digital health program includes a home blood pressure monitoring device, a digital platform (such as a mobile app or a web-based system), and a team of nurses who provide remote support and guidance to the patients. Patients are trained to use the home blood pressure monitoring device and the digital platform. They are instructed to measure their blood pressure at regular intervals, typically twice a day - once in the morning and once in the evening - and to record the readings on the digital platform. The platform allows for real-time transmission of the readings to the nurses, who monitor the data and provide feedback to the patients. The feedback may include advice on lifestyle modifications, medication adherence, and when to seek medical attention. The nurses also provide emotional support and education to the patients, helping them to understand their condition and the importance of blood pressure control [12]. Comparison: Usual care or any other non-nurse-led interventions for home blood pressure monitoring. Outcome: The primary outcome measure for this study is the change in blood pressure levels from baseline to the end of the intervention period. Blood pressure will be measured using standard procedures and the readings recorded on the digital platform will be used for analysis. A significant reduction in blood pressure levels will be considered as an indication of the effectiveness of the nurse-led digital health program in stroke patients [27]. Secondary outcomes include adherence to the program, patient satisfaction, and stroke recurrence. Adherence will be assessed by the frequency of blood pressure measurements recorded on the digital platform, with regular recordings indicating good adherence. Patient satisfaction will be evaluated using validated questionnaires, such as the Patient Satisfaction Questionnaire Short Form (PSQ-18) [28]. Stroke recurrence will be determined by reviewing the patients' medical records and/or self-reports. Study design: RCTs. Exclusion criteria include non-randomized studies, studies not reporting our outcomes of interest, and studies where the necessary data for the pooled analysis cannot be obtained. Studies with unclear definition of the nurse's role in the digital health program or where the intervention is not primarily nurse-led will also be excluded. Study Selection The process of study selection for the pooled analysis will involve several stages, starting with an initial screening of titles and abstracts. Two independent reviewers will conduct this screening to identify potentially relevant studies based on the PICOS criteria outlined in the eligibility criteria. Any disagreements between the reviewers will be resolved through discussion or by consulting a third reviewer. The full texts of the potentially relevant studies will then be obtained and assessed for eligibility. The same two reviewers will independently conduct this assessment, using a standardized form to ensure consistency. The form will include items related to the study population, intervention, comparison, outcomes, and study design. Studies that meet all the eligibility criteria will be included in the pooled analysis. A flow diagram following the PRISMA guidelines will be used to document the study selection process[25]. Data Extraction Data extraction will be performed using a standardized data extraction form that will be developed and pilot-tested on a few studies. Two reviewers will independently extract the data from the included studies, and any disagreements will be resolved through discussion or by consulting a senior reviewer. The data items to be extracted will include study characteristics (e.g., authors, year of publication, country), participant characteristics (e.g., age, gender, medical history), details of the intervention and comparison (e.g., components of the nurse-led digital health program, frequency and duration of blood pressure measurements), outcomes (e.g., blood pressure levels, adherence, patient satisfaction, stroke recurrence), and study results (e.g., effect estimates, confidence intervals). Risk of Bias Assessment The risk of bias in the individual studies will be assessed using the Cochrane Collaboration's tool for assessing risk of bias in randomized trials (RoB 2.0) [29]. This tool evaluates the risk of bias in five domains: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain is rated as low risk, some concerns, or high risk of bias. The overall risk of bias for each study is then determined based on the domain ratings. Two reviewers will independently conduct the risk of bias assessment, and any disagreements will be resolved through discussion or by consulting a third reviewer. The results of the risk of bias assessment will be presented in a 'Risk of bias' summary figure, following the Cochrane Handbook guidelines [25]. Grading of Evidence The grading of evidence in this protocol will be systematically conducted following the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. This method is recognized for its clear and transparent process of rating the quality of evidence and strength of recommendations [30]. For each outcome, the quality of evidence will be assessed across the domains of risk of bias, inconsistency, indirectness, imprecision, and publication bias. Evidence quality will be categorized into four levels: high, moderate, low, or very low. These ratings will reflect our confidence in the effect estimates and the likelihood that further research could impact our confidence in the estimate of effect or change the estimate. The GRADE approach will also allow us to consider the balance between desirable and undesirable effects, the values and preferences of patients, and resource implications, which are particularly relevant in the context of nurse-led digital health interventions for stroke patients. The results of the GRADE assessment will be presented in a 'Summary of Findings' table, which will provide a concise and user-friendly overview of the key information that is critical for decision-making [31]. Statistical analysis The data will be pooled and analyzed using meta-analysis techniques, if appropriate. The choice of a fixed-effect or random-effects model will depend on the level of heterogeneity among the included studies, which will be assessed using the I2 statistic [32]. If substantial heterogeneity is detected (I2 > 50%), a random-effects model will be used, and potential sources of heterogeneity will be investigated through subgroup analyses or meta-regression. Subgroup analyses will be performed based on predefined clinical or methodological characteristics. These may include, for example, the intensity of the intervention, duration of follow-up, or baseline blood pressure levels. If there is substantial heterogeneity that cannot be explained by subgroup analyses, we will conduct meta-regression analyses to investigate the impact of continuous variables on the effect size. To assess for publication bias, which may skew the results if studies with non-significant results are less likely to be published, we will visually inspect funnel plots for asymmetry for each meta-analyzed outcome when we have a sufficient number of studies (typically 10 or more). Additionally, we will employ more quantitative methods such as Egger's test to statistically assess the funnel plot asymmetry [33]. Sensitivity analyses will be conducted to evaluate the robustness of our findings by systematically excluding certain studies that do not meet specific quality criteria or by removing each study one at a time, we can assess the impact of individual studies on the overall meta-analysis results. The trim and fill method will be used in the presence of funnel plot asymmetry suggestive of publication bias. This non-parametric method estimates the number and outcomes of potentially missing studies and adjusts the meta-analysis to account for the hypothesized missing data, providing a more accurate estimate of the effect size [34]. For each outcome, the effect estimates (e.g., mean differences for continuous outcomes, risk ratios for dichotomous outcomes) and their 95% confidence intervals will be calculated. Forest plots will be used to graphically present the results of the meta-analysis. If meta-analysis is not appropriate due to high heterogeneity or other reasons, a narrative synthesis of the results will be conducted. The statistical analyses will be performed using Stata 12.0 software (Stata Corp LP, College Station, TX). Amendments The protocol governing this pooled analysis is designed to be a living document, subject to modifications and refinements as required throughout the course of the review. Adjustments to the protocol will be judiciously considered and implemented to ensure the review remains current and methodologically sound.