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The necessity of enhancing resuscitation training has been encouraged by The International Liaison Committee on Resuscitation and the American Heart Association to reduce mortality, disability and healthcare costs. Resuscitation training is a complicated approach that encompasses various components and their mixture. It is essential to identify the most effective of these components and their combinations, to measure the corresponding effect size and to understand which participant groups may enjoy the greatest advantage.
We will systematically search 12 databases and two clinical trial registries for randomised controlled trials (RCTs) that examine different resuscitation training methods from inception to April 2025. The analysis will be carried out using the standard network meta-analysis and component network meta-analysis models. Resuscitation skills of staff will be the primary outcome of this analysis. Paired reviewers will independently screen and extract data. A consensus will be sought with the principal investigators to resolve any disagreements that cannot be achieved through regular meetings. Each intervention in each RCT will be decomposed according to its constituent components, such as delivery method, interactivity, teamwork, digitalisation and type of simulator. The analysis will be conducted using the frequentist and bayesian approach in the R environment. RoB V.2.0 and Confidence in Network Meta-Analysis will, respectively, be used to assess the risk of bias and the certainty of the evidence.
As we will use only aggregated secondary data without individual identities, ethical approval is not required. Results of this review will be shared through a peer-reviewed publication and presentation of papers at any relevant conferences.
CRD42024532878
Despite evidence supporting nurse-led digitalized diabetes interventions, gaps persist in understanding their specific impact on community-dwelling patients with type 2 diabetes mellitus (T2DM). Prior reviews lacked a quantitative synthesis of these interventions' effects on outcomes like self-care, HbA1c, and quality of life (QoL), limiting their applicability to clinical practice. This study aimed to systematically evaluate and quantify the effectiveness of nurse-led digitalized diabetes management programmes for community-dwelling adults with T2DM.
We searched six databases to identify relevant articles from their inception to June 2024. Randomized controlled trials that evaluate the effects of nurse-led digitalized diabetes management programs for community-dwelling patients with T2DM were included. The Cochrane Risk of Bias tool version 2.0 was used to appraise the included studies. The pairwise meta-analysis was performed through the software Comprehensive Meta-Analysis Version 3.0.
Eleven RCTs were included, encompassing 2943 participants from various regions. Nurse-led digitalized programs significantly improved self-care behaviors (SMD = 1.15; 95% CI: 0.49 to 1.81), and QoL (SMD = 0.65; 95% CI: 0.37 to 0.94). The interventions also demonstrated a clinically meaningful reduction in HbA1c levels (MD = -0.25%; 95% CI: −0.43 to −0.06), highlighting their potential in improving glycaemic control. Heterogeneity across studies was substantial for self-care but moderate for HbA1c and QoL.
Nurse-led digitalised diabetes management programmes effectively enhance self-care behavior, reduce HbA1c levels, and improve QoL among community-dwelling patients with T2DM. These findings underscore the potential of digitalised interventions as scalable and accessible alternatives to traditional diabetes management, particularly in non-institutionalized settings.
Nurse-led digitalised diabetes management programmes can empower community-dwelling patients with T2DM to achieve better health outcomes by enhancing self-care and glycaemic control while improving QoL. Their integration into routine clinical practice could address barriers to care, optimize diabetes management, and reduce the long-term burden of the disease.
The International Prospective Register of Systematic Reviews (PROSPERO) identifier: CRD42024594874
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