Assessment Approach Research Articles

Culturally-informed neuropsychological evaluations of patients with spina bifida: Application of the ECLECTIC framework to a complex pediatric medical condition.

Objective: The pediatric patient populations in the United States are becoming increasingly diverse in culture. In addition to medical factors, a variety of sociocultural factors (including educational systems, language, immigration status, etc.) can impact a child's cognitive development, performance on traditional neuropsychological measures, provider interactions, differential diagnosis, and recommendations. A culturally-informed neuropsychological evaluation aims to understand the impact of sociocultural factors and integrate them into the assessment approach. Fujii's ECLECTIC framework (acronym for Education & Literacy, Culture/Acculturation, Language, Economic Issues, Communication Style, Testing Situation, Intelligence Conceptualization, and Context of Immigration) can be used to guide culturally-informed neuropsychological evaluations of children with complex medical conditions. Method: Using the ECLECTIC framework, this paper describes clinical considerations and offers practical solutions for providing culturally-informed pediatric neuropsychology evaluations. Four diverse patients with spina bifida are reviewed. Results: The case presentations highlight the application of the ECLECTIC framework within a medically complex pediatric patient population. Conclusions: Spina bifida is one example of a pediatric medical population in which both medical and sociocultural factors can impact cognitive development and the evaluation process. The ECLECTIC framework defines a variety of sociocultural factors that can influence cognitive development and multiple aspects of a pediatric neuropsychological evaluation. Pediatric neuropsychologists should use the ECLECTIC framework to provide culturally-informed evaluations for our increasingly diverse patient populations.

A new deep learning-based approach for concrete crack identification and damage assessment

A new deep learning-based approach for concrete crack identification and damage assessment

An active learning approach for reliability assessment of passive systems combining polynomial chaos expansion and adaptive sampling

Passive safety system is extensively employed in newly designed reactors to increase their inherent safety. However, the driving force is smaller, and the uncertainty of the thermal–hydraulic (T-H) process may potentially result in the inability of system to perform its intended function, which is often known as functional failure. Therefore, the passive systems reliability has received increasing attention. Unfortunately, the assessment of system failure probability utilizing Monte Carlo simulation (MCS) methods necessitates a significant quantity of repeated calculations employing the thermal–hydraulic code, which may be impractical in terms of computational cost. To enhance the assessment calculation efficiency, an adaptive surrogate model is proposed. The bootstrap method was employed to introduce an error estimate into the polynomial chaos expansion (PCE) model, which solves the obstacle of the PCE model in reliability evaluation applications due to the lack of error estimation. The method constructs an active learning approach through error estimation, strategically identifies and selects the best candidate samples, and iteratively updates the initial experimental design. The effectiveness of the method was verified through three benchmark cases, and it was employed for assessing the passive residual heat removal system (PRHRS) reliability within integral-type pressurized water reactor (IPWR200). The results demonstrated that the proposed method significantly diminishes the frequency of invocations to the T-H code and improves computational efficiency to a large extent. Furthermore, it can serve as a valuable guide for the design, operation, and reliability evaluation of the passive system.

A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation

This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI’s diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain.

Small-volume blood sample collection tubes in adult intensive care units: A rapid practice guideline.

This Intensive Care Medicine Rapid Practice Guideline (ICM-RPG) provides an evidence-based recommendation to address the question: in adult patients in intensive care units (ICUs), should we use small-volume or conventional blood collection tubes? We included 23 panelists in 8 countries and assessed and managed financial and intellectual conflicts of interest. Methodological support was provided by the Guidelines in Intensive Care, Development, and Evaluation (GUIDE) group. We conducted a systematic review, including evidence from observational and randomized studies. Using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach, we evaluated the certainty of evidence and developed recommendations using the Evidence-to-Decision framework. We identified 8 studies (1 cluster and 2 patient-level randomized trials; 5 observational studies) comparing small-volume to conventional tubes. We had high certainty evidence that small-volume tubes reduce daily and cumulative blood sampling volume; and moderate certainty evidence that they reduce the risk of transfusion and mean number of red blood cell units transfused, but these estimates were limited by imprecision. We had high certainty that small-volume tubes have a similar rate of specimens with insufficient quantity. The panel considered that the desirable effects of small-volume tubes outweigh the undesirable effects, are less wasteful of resources, and are feasible, as demonstrated by successful implementation across multiple countries, although there are upfront implementation costs to validate small-volume tubes on laboratory instrumentation. This ICM-RPG panel made a strong recommendation for the use of small-volume sample collection tubes in adult ICUs based on overall moderate certainty evidence.

Environmental analysis and comparison of the conventional and fractal glass textured surface photovoltaic panels

There are two major forms of solar energy that are typically utilized: photovoltaic and concentrated applications. The application of fractal glass texture to photovoltaic solar panels is a cutting-edge technique in the field of solar panels that generate electricity from exposure to light. When it comes to studying the environmental implications of a product during its development and commercialization, the life cycle assessment (LCA) approach is an excellent technique that can be utilized. The purpose of this study is to offer a thorough understanding of the product’s effects on the environment by taking into consideration a wide range of criteria, including environmental, economic, and other evaluations. Through the utilization of the life cycle assessment (LCA) methodology and the SimaPro software, this paper presents a comparative analysis of conventional solar panels and fractal glass texture panels. During the course of this research, 18 midpoint indicators and three endpoint indices were investigated. In addition, a sensitivity analysis has been carried out on the fractal property of the panel in order to evaluate the impact that it has on environmental impacts and damages. For the purpose of this study, three distinct levels of fractal coating were applied to the panel surface: one percent, three percent, and five percent. As a result of the data, it was determined that the “Photovoltaic cell single-Si wafer” and the “Transport, freight, sea, transoceanic tanker” had the most significant impact on the midpoint and endpoint indices for both panels, respectively. Furthermore, there is a direct association between the rise in fractal coating on panels and the reduction in environmental repercussions, approximately. This correlation exists because of several factors.

Flood preparedness in rural flood-prone area: a holistic assessment approach in Bangladesh

Despite the many fatalities and injuries, Bangladesh lacks holistic individual flood preparedness studies. This study evaluates flood preparedness in the flood-prone rural region of Dowarabazar Upazila, Sunamganj District, Bangladesh. The field survey had 596 respondents. As required, we implemented Spearman’s rank correlation and multiple linear regression. Flooding affected the study population, which made people feel extremely unsafe. Most respondents warned that floods might affect their homes. Many participants reported receiving flood-related information from locals. Most participants reported not being notified of flood forecasts and warnings in their area. The majority indicated that they sought refuge in flood shelters. A significant portion of the participants in the study were unaware of flood preparedness. Loss minimization and adaptability are deficiencies. Concerning flood preparedness, gender, age, occupation, monthly income, multiple sources of income, and house type are all significant determinants. According to field research, females are less prepared for flooding than males. Individuals with pucca housing are better equipped than those with unsafe housing (kacha). Authorities should thus take these factors into account while enhancing flood preparedness. In addition to implementing measures such as improving livelihood patterns and conducting educational campaigns, it is imperative to construct holistic flood preparedness.

Exploring ensemble-based approaches for granular suicide risk assessment: a comprehensive framework in therapeutic informatics

Exploring ensemble-based approaches for granular suicide risk assessment: a comprehensive framework in therapeutic informatics

Study on controllability assessment of a new type of power system based on lagrangian function integrated assessment method

The complex structure and multiple controllability metrics of the new power system require a comprehensive assessment approach. We propose a controllability assessment method based on the Lagrangian function, analyzing key factors that influence controllability. Our index system includes distributed power supply utilization rate, energy storage station charge state, power factor, and voltage offset. Using Lagrange shadow price theory, we construct a guideline layer index system treating bottom indexes as constraints. We formulate an optimization function utilizing the Lagrangian shadow price theory and considering weights calculated from the bottom index’s shadow price. These weights inform primary evaluation. By combining primary evaluation results, weights, and variable coefficients using the percentage system method, we determine comprehensive evaluation results for the new power system’s controllability. Experimental results demonstrate the high reliability of this method’s evaluation indexes, indicating their effectiveness. This research provides valuable insight for the advancement of the new power system. Region 1 had the lowest overall evaluation score of 51.57 and was rated general. The overall score of Region 2 was 77.24, which was considered good. Region 3 received the highest overall score of 86.29 and was rated as excellent.Graphical

Linearity assessment: deviation from linearity and residual of linear regression approaches.

In this computer simulation study, we examine four different statistical approaches of linearity assessment, including two variants of deviation from linearity (individual (IDL) and averaged (AD)), along with detection capabilities of residuals of linear regression (individual and averaged). From the results of the simulation, the following broad suggestions are provided to laboratory practitioners when performing linearity assessment. A high imprecision can challenge linearity investigations by producing a high false positive rate or low power of detection. Therefore, the imprecision of the measurement procedure should be considered when interpreting linearity assessment results. In the presence of high imprecision, the results of linearity assessment should be interpreted with caution. Different linearity assessment approaches examined in this study performed well under different analytical scenarios. For optimal outcomes, a considered and tailored study design should be implemented. With the exception of specific scenarios, both ADL and IDL methods were suboptimal for the assessment of linearity compared. When imprecision is low (3 %), averaged residual of linear regression with triplicate measurements and a non-linearity acceptance limit of 5 % produces<5 % false positive rates and a high power for detection of non-linearity of>70 % across different types and degrees of non-linearity. Detection of departures from linearity are difficult to identify in practice and enhanced methods of detection need development.

Prophylactic embolization versus observation for high-grade blunt trauma splenic injury: a systematic review with meta-analysis.

The objective of this systematic review was to compare the effectiveness of prophylactic angioembolization with observation as primary management strategies for patients with high-grade (grades 3-5) blunt trauma splenic injury. The spleen is frequently injured in abdominal trauma. Historical management practices involved splenectomy, but more recent evidence suggests an increased risk of severe infections and sepsis associated with this approach. Accordingly, non-operative management strategies, including prophylactic splenic artery embolization and clinical observation, have gained prominence. This systematic review with meta-analysis directly compares angioembolization with clinical observation for high-grade splenic injuries only, aiming to provide clarity on this matter amid ongoing debates and variations in clinical practice. This review included adult patients aged 15 years or older with high-grade splenic injuries (grade 3-5) due to blunt trauma. Outcomes of interest include the need for further intervention (failure of management), mortality, complications, red blood cell transfusion requirements, hospital length of stay, and intensive care unit length of stay. A comprehensive search of PubMed, Embase, and CINAHL (EBSCOhost), was performed with no restrictions on language or publication date. Gray literature was searched, including trial registries and relevant conference proceedings. After deduplication, 2 reviewers independently assessed titles and abstracts, and, subsequently, full-text articles for eligibility. Methodological quality of the included studies was assessed using standardized instruments from JBI. Data was extracted using predefined templates, and statistical meta-analysis was performed, where possible, using a random effects model. Heterogeneity was assessed using statistical methods, and potential publication bias was tested with a funnel plot. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach was used to evaluate the certainty of evidence. Sixteen studies were included in this review. Methodological quality assessment indicated some risk of bias in most studies, with concerns primarily related to differences in injury severity and potential confounding factors. Meta-analysis revealed that prophylactic angioembolization significantly reduced risk of management failure by 57% (OR 0.43, 95% CI 0.28-0.68, I2=53%, 15 studies) and decreased patient mortality by 37% (OR 0.63, 95% CI 0.43-0.93, I2=0%, 9 studies) compared with clinical observation alone. There was a 47% reduction in risk of complications associated with prophylactic embolization compared with clinical observation (OR 0.53, 95% CI 0.29-0.95, I2=0%, 4 studies). Some statistical heterogeneity was observed, with I2 ranging from 0% to 53%. No significant differences were observed between the 2 management strategies for red blood cell transfusion requirements and hospital length of stay. The results of this study support the use of prophylactic embolization for high-grade blunt trauma splenic injuries, indicated by lower failure of management rates, reduced need for additional interventions, lower mortality, and fewer complications. PROSPERO CRD42023420220.

Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training.

Development of LC–MS/MS Method for the Simultaneous Detection of Emerging Contaminants in Aquatic Matrices

Emerging contaminants (ECs) are substances that have no defined environmental quality standards or regulations, and have the potential to pose major adverse impacts on the environment and human health. The detection of contaminants in the natural environment is the key step for establishing precise environmental risk assessment approach for ECs. However, ECs come from different origins with various physicochemical properties, making their detection a complicated process. Moreover, their presence in the aquatic environment at trace concentration range (ng/L-µg/L), requires an accurate detection at low concentration levels. This study aims to develop an efficient analytical method for simultaneous determination of 5 different ECs in aqueous solution based on solid phase extraction technique (SPE) followed by liquid chromatography-mass spectrometry (LC–MS/MS). High recovery rates (72% to 114%) were achieved for all targeted compounds. Ciprofloxacin (CIP), diuron (DIU), terbutryn (TER) and diclofenac (DIC) had a limit of detection (LOD) of 5 µg/L and a limit of quantification (LOQ) of 10 ng/L, while LOD and LOQ for EE2 were 25 µg/L and 50 ng/L, respectively. These results confirm that the optimized method can be applied for extraction and analysis of ECs from different classes in the aquatic environment.

A comprehensive approach for assessing the causes of low productivity in the construction sector: a systematic categorization and ranking using Pareto and Fuzzy analysis

Implementing a comprehensive and globally adaptable assessment approach for causes of low construction productivity has proven to be a contemporary challenge. This prevails since the factors influencing the construction industry vary significantly by geographic region and operational characteristics. Hence, substantial research on this topic has restricted its scope to a particular location without a comprehensive global categorization. Furthermore, much of this research has neglected the influence of subjectivity in the stakeholder response evaluations. To address these shortcomings, this study provides a unique assessment of the construction context initiated by a systematic review of 130 studies published worldwide over the previous 32 years. The 915 unique factors from this systematic review were then categorized into regional and productivity dimensions through Pareto and Frequency analysis techniques. The regional categorization encompassed nine distinct regions spanning the world, while the productivity component encompassed a set of 14 distinctive benchmarks that characterize productivity in the construction industry. These sorted factors were then adapted in the Sri Lankan context as a pilot study through a cross-sectional survey including 117 stakeholders. The following Fuzzy analysis allowed the mitigation of the ambiguity of these survey responses while ranking the factors based on a calculated importance index while also considering the interdependence of the introduced benchmarks. Ultimately, the employed assessment approach for identifying and ranking factors contributing to low construction productivity in this study is proposed as globally adaptable with its successful incorporation of subjective evaluation.

Conceptualizing disparities and differences in the psychobiology of traumatic stress.

Understanding biological pathways that mediate trauma-related psychopathology is a major goal for traumatic stress studies. There is growing interest in studying differences in neural, physiological, and behavioral correlates of traumatic stress across demographic groups (e.g., sex/gender, race/ethnicity). However, challenges remain in how to appropriately conceptualize the source, mechanisms, and practical utility of between-group variation. The present brief conceptual review discusses ethnicity, race, and sex/gender-related variability relevant to understanding the psychobiology of traumatic stress in the context of traumatic stress studies. We discuss recent evidence related to socioenvironmental influences on ethnoracial variability in the brain and behavior relevant to traumatic stress, as well as sex/gender associations in neurophysiology that may contribute to the development of adverse posttraumatic sequelae. We further synthesize these findings by discussing intersectional influences of sex/gender- and race/ethnicity-related factors on trauma-related physical and mental health outcomes. The present review provides an important foundation for future research on disparities and individual differences in traumatic stress to move the field toward more effective assessment and treatment approaches.

Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW- ARAS Under Trapezoidal Fuzzy Set

In this study, a novel risk assessment framework designed for evaluating the challenges of plastic packaging waste management in the context of reverse logistics is introduced. The framework leverages Failure Mode Effect Analysis (FMEA) to address decision-making in a fuzzy environment. To augment the traditional FMEA risk criteria, encompassing severity (S), occurrence (O), and detection (D), three additional essential risk criteria are introduced: cost of failure (C), complexity of failure resolution (H), and impact on business (I). These newly incorporated criteria significantly enhance the capacity to convey the multifaceted risks inherent in reverse logistics for the plastic recycling sector. Furthermore, a comprehensive literature review and expert validation are conducted to identify ten distinct failure modes. To subjectively and objectively determine the risk criteria weightings, a combination of Analytic Hierarchy Process (AHP) and LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) is employed. Finally, the Additive Ratio Assessment (ARAS) approach is applied to prioritize such failure modes. Recognizing the inherent imprecision and uncertainty associated with human decision-making, the trapezoidal fuzzy set (TrFS) is adopted throughout all decision-making processes. To showcase the proposed framework effectiveness, the framework is applied as a case study to a waste plastic recycling manufacturer in Thailand.

A tale of two centres: A cross-institutional peer review of integrated assessment use in direct entry programs

Direct Entry Programs (DEPs) at Australian universities are designed to enhance students’ English language and academic skills, with the primary goals of preparing and assuring students’ readiness for university studies. Given that university tasks often require the integration of language skills (e.g., writing assignments using information from lectures, articles, and textbooks), this paper proposes the use of integrated assessment tasks in DEPs to increase the authenticity in their assessment approach by using tasks that replicate actual university learning, teaching, and assessment practices. To encourage the shift from standard practice of separate skills testing to integrated assessment, we present key findings of a literature review in integrated assessment use in language programs and show how integrated assessment is implemented in two institutions in Sydney, Australia. The paper also highlights advantages of cross-institutional peer review and proposes an argument-based validity framework for test design that language centres can use to guide good practice.

Thrombogenicity assessment of perfusable tissue engineered constructs: a systematic review.

Vascular surgery faces a critical demand for novel vascular grafts that are biocompatible and thromboresistant. This urgency particularly applies to bypass operations involving small caliber vessels. In the realm of tissue engineering, the development of fully vascularized organs holds great promise as a solution to organ shortage for transplantation. To achieve this, it is imperative to (re-)construct a biocompatible and non-thrombogenic vascular network within these organs. In this systematic review, we identify, classify and discuss basic principles and methods used to perform in vitro/ex vivo dynamic thrombogenicity testing of perfusable tissue engineered organs and tissues. We conducted a pre-registered systematic review of studies published in the last 23 years according to PRISMA-P Guidelines, comprising a systematic data extraction, in-depth analysis and risk of bias assessment of 116 included studies. We identified shaking (n=28), flow loop (n=17), ex vivo (arterio-venous shunt, n=33) and dynamic in vitro models (n=38) as main approaches for thrombogenicity assessment. This comprehensive review unveils a prevalent lack of standardization and serves as a valuable guide in the design of standardized experimental setups.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Effectiveness of eHealth Interventions in Improving Medication Adherence Among Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis.

Nonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue. The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs). A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. A total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention. The research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients' medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed. INPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/.