Central Index Research Articles

Increasing Yield more than Maintaining Fertile Soils Motivated the Choice of Climate-Smart Soil Technologies among Greater Lira Pigeon Pea Farmers

Pigeon pea farmers in Gerater Lira, Uganda, fight the adverse effects of climate change and deteriorating soil fertility. They are combining different strategies and adopting climate-smart soil technologies (CSS technologies) to maintain fertile soil and increase yields. The study explored how the motive for maintaining fertile soils affected CSS technologies' choice. A sample of 39 farmers participated in laddering interviews. Data was analyzed by the means-end chain (MEC) framework and the centrality index (CI) technique. MEC results indicate that farmers predominantly linked crop diversification, addressed dietary needs, increased yields, and increased incomes. In addition, they paid less attention to maintaining fertile soils. Results of the CI highlight goal priorities by gender subgroups with females aged at least 40 paying more attention to producing food, soil fertility, and improving health, while male farmers of the same age category were inclined to spread production risk. Results further showed that male farmers below 40 years of age tend to produce for markets and benevolent, while their female counterparts tend to maintain soil nutrients. Our overall findings could help in the development of targeted strategies to encourage a wider spread of CSS technology use for climate-smart agriculture. This could enhance agricultural resilience in the face of climate change. We recommend encouraging farmers to apply CSS technologies while considering the long-term effects they might have on soil fertility. we further recommend that farmers intensify residual retention to improve soil fertility without requiring money to purchase inorganic fertilizer.

A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain

This paper proposes the use of Complex Network Analysis tools to study the spatial distribution and temporal evolution of groundwater abstraction wells within a region of interest. To that end a weighted undirected network connecting wells within a certain distance was built. The edge weights take into account the distance between the wells as well as their corresponding abstraction volumes. In addition to the conventional complex network metrics, two specific abstraction-based centrality indexes are proposed, based on the total impact of a groundwater abstraction well on its surrounding wells and on the total impact of surrounding wells on a specific location. The proposed approach has been applied to the area of the Doñana Natural Space, one of the most important wetlands in Europe. The topology of the network reveals a large number of small, disconnected components and a few large components. This is reflected also in the degree distribution, which follows a Power Law distribution. The network has a large average clustering coefficient, a small average path length and a low level of centralisation. Global and average local network efficiency are also high. The network is assortative in terms of degree-degree and strength-strength correlations. Eigenvector centrality of the nodes reveals the location of influential wells.

The Network Structure of School Principal Resilience: Gender Differences

ABSTRACTThe ability of school principals to demonstrate resilience enables them to effectively lead, adapt and thrive amidst the ever‐changing landscape of educational challenges and transformations. Despite its necessity, research on principal resilience remains largely underdeveloped and is still in its early stages. The current study aims to fill this gap using network analysis to examine the inter‐structural principal resilience network and gender differences in terms of network estimation, centrality indices, network stability and network comparison using a sample of 1274 principals from China. The results reveal a comprehensive understanding of the dynamic network and gender effects on school principal resilience at the dimension and item levels. It is noted that social resilience exhibits the highest strength, indicating the critical importance of social support networks and relationships. The gender analysis indicated that spiritual resilience emerged as a central dimension in the resilience network of female principals, while emotional and psychological resilience was the central dimension in the resilience network of male principals. This study advances the conceptual and practical understandings of school principal resilience by examining the interactions among different dimensions and items, thereby contributing to the maturity of knowledge production and intervention efforts in the field.

Interconnectedness of mindfulness facets and their impact on mental health outcomes in Asian American and European American college students: A network approach.

Despite extensive research supporting the effectiveness of mindfulness-based interventions, the understanding of the dynamic connections between various mindfulness facets, particularly across diverse cultures, remained limited. This study aimed to investigate the networks among mindfulness aspects across different cultural backgrounds and their individual associations with mental health and well-being. Using the data collected from 710 undergraduate students in Hawaii (445 Asian Americans, 265 European Americans), we constructed sparse networks for each group to investigate their centrality index. To compare the overall structures of these networks, we utilized permutation-based tests. In addition, we used relative weight analysis to evaluate the distinct contributions of each mindfulness facet to positive and negative mental health outcomes. The results indicated similar trends in both racial groups, emphasizing the importance of specific mindfulness aspects like describing and acting with awareness within the mindfulness construct. However, notable variations were observed, particularly in how observing and nonjudging facets related among Asian Americans. In addition, acting with awareness and nonjudging showed substantial inverse associations with negative mental health outcomes across groups, whereas the observing aspect displayed positive connections with anxiety and stress among Asian Americans. The findings suggest which aspects are fundamental to mindfulness, aiding in a nuanced understanding of the construct. They also emphasize the importance of acknowledging cultural diversity in both mindfulness research and its clinical applications. Practical implications and directions for future research were discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhancing non-invasive pre-surgical evaluation through functional connectivity and graph theory in drug-resistant focal epilepsy

BackgroundEpilepsy, characterized as a network disorder, involves widely distributed areas following seizure propagation from a limited onset zone. Accurate delineation of the epileptogenic zone (EZ) is crucial for successful surgery in drug-resistant focal epilepsy. While visual analysis of scalp electroencephalogram (EEG) primarily elucidates seizure spreading patterns, we employed brain connectivity techniques and graph theory principles during the pre-ictal to ictal transition to define the epileptogenic network. MethodCortical sources were reconstructed from 40-channel scalp EEG in five patients during pre-surgical evaluation for focal drug-resistant epilepsy. Temporal Granger connectivity was estimated ten seconds before seizure and at seizure onset. Results have been analyzed using some centrality indices taken from Graph theory (Outdegree, Hubness). A new lateralization index is proposed by taking into account the sum of the most relevant hubness values across left and right regions of interest. ResultsIn three patients with positive surgical outcomes, analysis of the most relevant Hubness regions closely aligned with clinical hypotheses, demonstrating consistency in EZ lateralization and location. In one patient, the method provides unreliable results due to the abundant movement artifacts preceding the seizure. In a fifth patient with poor surgical outcome, the proposed method suggests a wider epileptic network compared with the clinically suspected EZ, providing intriguing new indications beyond those obtained with traditional electro-clinical analysis. ConclusionsThe proposed method could serve as an additional tool during pre-surgical non-invasive evaluation, complementing data obtained from EEG visual inspection. It represents a first step toward a more sophisticated analysis of seizure onset based on connectivity imbalances, electrical propagation, and graph theory principles.

A network analysis of voice hearing, emotional distress and subjective recovery before and after cognitive behavioural interventions.

There is a need to identify and to better understand key processes involved in voice hearing, which can inform the targeting and development of psychological interventions for distressing voices. The current study aimed to examine interrelations between the negative impact of voices, voice characteristics, emotional distress and recovery before and after cognitive behavioural interventions for voices (Coping Strategy Enhancement, guided self-help Cognitive Behavioural Therapy, Relating Therapy and Person-Based Cognitive Therapy). The sample consisted of 172 participants from the Sussex Voices Clinic who completed pre- and post-treatment assessments. The negative impact of voices, voice characteristics, emotional distress and recovery were used to estimate two networks, before and after cognitive behavioural interventions, using the graphical lasso method with the extended Bayesian information criterion. Centrality indices were also computed, and the two networks were compared on connectivity, structure and individual edge weights. Depression, anxiety and the negative impact of voices were identified as key central symptoms and acted as bridge symptoms in pre- and post-treatment networks. There were no significant differences in network structure (M = 0.155, p = .57), global strength (S = 0.188, p = .07) and centralities (C = -0.318, p = -.06) between the two networks. Our findings suggest that anxiety and depression are promising treatment targets, that can lead to reductions in voice-related distress, whereas the characteristics of voices and subjective recovery play little role in the network structure. Limitations include the lack of a control group and the lack of diversity within the sample.

Characterizing impulsivity in individuals with methamphetamine and methcathinone use disorders.

Individuals with substance use disorder (SUD) are characterized by loss of control in drug use, such as increased impulsivity. Methamphetamine and methcathinone are psychostimulants, the use of which is accompanied by a high level of impulsivity. Whether individuals with methamphetamine use disorder (MUD) and methcathinone use disorder (MCUD) differ in different aspects of impulsivity is unclear. We investigated impulsivity traits and behaviors in individuals with MUD and MCUD. The Barratt Impulsiveness Scale (BIS), Sensation Seeking Scale (SSS), and delay discounting task (DDT) were assessed in individuals with MUD and MCUD and in healthy controls (HCs); then, we performed network-based analysis and computational modeling to understand the potential differences among the three groups. MUD subjects scored higher than MCUD subjects in terms of motor impulsivity, nonplanning impulsivity, and total BIS scores. The network analysis revealed that there were no significant differences between MUD and MCUD subjects in any centrality indices. The discount rate of MUD and MCUD subjects was significantly greater than that of HCs, whereas there was no difference in the discount rate between the two addiction groups. These findings suggest that MUD and MCUD participants differ in impulsivity traits but not in impulsive behaviors, implying that impulsive traits and behaviors represent different aspects of impulsivity.

Modeling and Analysis of Public Transport Network in Hohhot Based on Complex Network

In the urban public transport network, the transfer of buses and subways provides convenience for residents to travel efficiently. But in actual operation, it is found that accidents, natural disasters, and other damage are inevitable. These sudden events may lead to route suspensions and service delays, ultimately resulting in network paralysis. In this paper, complex network theory is used to construct a weighted double-layer network model. Carrying capacity is considered the edge weight. The model analyzes the impact of these sudden events on network performance. It also conducts in-depth research on network structure and node importance. A collective influence (CI) algorithm is proposed as a centrality index to evaluate node importance. Based on the dynamic nature of the attacks, the network state is divided into initial network and current network. Taking Hohhot as an example, the results show that the network based on a CI algorithm node attack has the worst invulnerability. The network invulnerability based on an edge weight attack is better than that of edge betweenness. Compared with the current network, the invulnerability of the initial network is stronger. This indicates that ongoing changes and adaptations in the network may accelerate the decline in overall performance. At the same time, targeted interventions on key nodes and edges can enhance the network’s invulnerability. Planners can continuously monitor network performance to provide a basis for dynamic management and real-time adjustments. Additionally, effective information about critical routes to the public helps ensure the sustainable operation of the public transportation network.

PREDICTING LAND USE CHANGES USING MARKOV CHAIN ANALYSIS AND HIERARCHICAL ANALYSIS OF PUBLIC SERVICE CENTERS IN BANDA ACEH CITY

The city of Banda Aceh, as the center of development in its region, is faced with a series of complex problems, one of which is the lack of optimal distribution of public service centers throughout the city. Banda Aceh City Central Statistics Agency (BPS) documents 2012-2022 and Banda Aceh City Spatial Plan (RTRW) documents 2009-2029 related to this research. Data in tabulated and descriptive form includes geographical conditions of the research area, land use area, types of facilities and their numbers, population, and distance between areas, and maps related to the research. This study aims to see land use conditions using the markov chain method and provide an overview of the public service center system of Banda Aceh City using the Scalogram method, centrality index, and gravity. Based on Markov chain analysis, land use predictions indicate that residential areas, offices and trade, tourism, worship, and sports facilities will continue to increase, while water bodies, green open spaces (RTH), and non-green open spaces (RTNH) will continue to decline. Predictions until 2039 show that conditions have begun to stabilize. Scalogram analysis takes into account hierarchy based on the type of facilities available, centrality index that calculates hierarchy based on many available facilities, and gravitational interaction that takes into account the strength of interaction between sub-districts shows that Kuta Alam District has the potential to become a major service center in Banda Aceh City. This sub-district has the most complete facilities, supported by the highest interaction value.

Loop analysis quantifying important species in a marine food web

Improving the predictive power of food web analysis is a major challenge. Identifying the relationships that link topological and dynamical features may help. We used the predictions of loop analysis about the effect of perturbations targeted to the components of Barents sea food web to quantify their sensitivity and community impact, that we summarized in two new indices, NI and NS. Using a multivariate analysis we interpreted the meaning of these indices in a benchmarking exercise using several well recognized indices of species topological (positional) importance. Our findings suggest that the information the two indices proposed here provides does not overlap with that of more diffused topological indices of positional importance (i.e. centrality indices). The former are express the dynamic consequences of the topology in which species are embedded, whereas for the latter such dynamical consequences are mostly hypothesized on a topological base. The indices of loop analysis are based on the effective role a species plays in passing the impacts to other species (NI) and their role as sinks of the perturbations entering anywhere in the system (NS). These two indices, in the end, reveal how the topology of the network affects the response of the species to perturbations and thus emphasize the interaction between topology and dynamics. Based on our results, the question related to conservation is whether to prioritize sensitive species, that can be more strongly influenced when others are perturbed, or species of high impact, that can more strongly influence the rest of the community if perturbed.

Symptom clusters and symptom network analysis during immunotherapy in lung cancer patients.

This study analyzes symptoms in lung cancer patients undergoing immunotherapy to identify core symptom clusters through network analysis and lay a foundation for effective symptom management programs. The sample comprised 240 lung cancer patients receiving immunotherapy. Participants were assessed using the Memorial Symptom Assessment Scale. Exploratory factor analysis was used to extract symptom clusters, and network analysis using JASP 0.17.3 was performed to explore the centrality indices and density of the symptom network. Five symptom clusters were identified, i.e., emotion-related, lung cancer-related, physical, skin, and neural symptom clusters, with a cumulative variance contribution rate of 55.819%. Network analysis revealed that sadness was the most intense symptom (rs = 2.189), dizziness was the most central symptom (rc = 1.388), and fatigue was the most significant bridging symptom (rb = 2.575). This study identified five symptom clusters and a symptom network among lung cancer patients during immunotherapy. The network analysis's centrality indices and network density results can assist healthcare professionals in devising more precise symptom management strategies.

Pola Spasial Pergeseran Pusat Pertumbuhan Desa di Kabupaten Kulon Progo

Kulon Progo is a poorest district and has the highest development inequality in Daerah Istimewa Yogyakarta (DIY) based on Badan Pusat Statistik (BPS) publication. The local government has made various efforts to overcome the problems, one of which is by establishing growth centers since 2003. However, Kulon Progo Regency has experienced rapid development with the existence of Yogyakarta International Airport, which affected the dynamics of regional growth centers. The purpose of this study is to identify the potential Rural Growth Center (PPD) and identify the spatial patterns of PPD shifts in Kulon Progo to see the trickle down effect. This study used descriptive quantitative methods using scalogram index, centrality index, urban network analysis, and gravity index, also moran’s I index and Local Indicator Of Spatial Association (LISA). The results showed that there was a shift in rural growth centers, but the spatial analysis did not show any trickle down effect from rural growth centers that had been formed before. However, new centers were formed that were not related to the previous rural growth centers. These results indicates that the rural growth centers in Kulon Progo cannot have a growth effect on the surrounding area.

Using network analysis to provide evidence for brain health as a unified construct relevant to aging with HIV.

Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV. Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network. The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops," indicating strong connectivity. The most central symptoms were "How much do you enjoy life?" and "How often do you have negative feelings?". The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.

Exploring core and bridge symptoms in patients recovering from stroke: a network analysis.

Patients recovering from stroke experience a variety of symptoms that present as a synergistic and mutually reinforcing "symptom cluster," rather than as singular symptoms. In this study, we researched and systematic analyzed these symptom clusters, including core and bridge symptoms, to help determine the relationships between symptoms and to identify key symptom targets, providing a new approach for formulating precise symptom management interventions. Convenience sampling was applied to select 432 stroke recovery patients treated in the Seventh People's Hospital of Changzhou City from August 1, 2023 to April 14, 2024. Subsequently, a cross-sectional survey was conducted using the General Information Questionnaire and Stroke Symptom Experience Scale to extract symptom clusters via exploratory factor analysis. Finally, the "qgraph" and "bootnet" packages in the R language were used to construct a network layout to describe the relationships between symptoms and calculate the centrality index. The average age of the 432 enrolled recovering stroke patients was 68.17 ± 12.14 years, including 268 males (62.04%) and 164 females (37.96%), none of whom underwent surgical intervention. Among this cohort, the 3 symptoms with the highest incidence rates were "limb weakness" (A2, 80.56%), "fatigue" (A5, 77.78%), and "limitations of limb movement" (A1, 68.06%). A total of 5 symptom clusters were extracted: the somatic activity disorder, mood-disorder-related, cognitive-linguistic dysfunction, somatic-pain-related, and foot dysfunction symptom clusters. In the symptom network, the 2 most common symptoms in terms of intensity and expected impact were "fatigue" (A5, rs = 1.14, re = 1.00) and "pessimism about the future" (B3, rs = 1.09, re = 1.02). The symptom with the strongest bridge intensity was "limb pain" (D1, rs = 2.64). This study uses symptom network analysis to explore the symptoms of stroke patients during recovery, identifying core symptoms and bridge symptoms. Based on these findings, we can develop more targeted management plans to improve the accuracy and efficiency of interventions. Through this management approach, we can enhance treatment effectiveness, reduce unnecessary medication, lower adverse drug reactions, and optimize the allocation of medical resources.

Network Analysis of Association Between Problematic Social Network Use and Alexithymia in Freshmen.

Exploring the core and bridge nodes in problematic social network use and alexithymia among freshmen to provide a basis for understanding the relationship and interventions. A total of 4057 first-year students from four universities in Shandong Province were chosen and surveyed with the Problematic Mobile Social Media Use Assessment Questionnaire and the Toronto Alexithymia Scale (TAS). Network analysis was performed using R to estimate the connections between nodes. Centrality and predictability indicators were used to identify key nodes, with accuracy and stability validation techniques applied. Gender and residence differences in the network structure were also examined. In the problematic social network use network, the nodes with the highest expected influence were P16 (excessive swiping) and P14 (lack of control over phone usage). In the problematic social network use-alexithymia network, cognitive failure had the highest strength (strength = 1.155) and centrality. Difficulty identifying feelings (bridgestrength = 0.32), externally oriented thoughts (bridgestrength = 0.24), and cognitive failure (bridgestrength = 0.19) were key bridge nodes. No significant differences were found in the network structure across gender and residence, though the network was tightly connected. Cognitive failure plays a central role in problematic social network use among freshmen. Difficulty identifying feelings, externally oriented thoughts, and cognitive failure are critical in linking problematic social network use with alexithymia.

Network Analyses Applied to the Dimensions of Cancer-Related Fatigue in Women With Breast Cancer.

Understanding cancer symptom cluster through network analyses is a new approach in oncology, revealing interconnected and influential relationships among reported symptoms. We aimed to assess these relationships using network analysis in posttreatment breast cancer patients, focusing on the five dimensions of cancer-related fatigue (CRF), and on other common difficulties encountered by oncological patients (i.e., pain, anxiety, depression, sleep difficulties, cognitive impairments, and emotion regulation and mental adaptation difficulties). This study involved a complementary analysis of data from two interventional studies. Participants completed questionnaires before and after the intervention, with baseline scores being used in this article. Partial correlation network analysis modeled the relationships between symptoms in five distinct networks, each of them including one specific dimension of CRF. The core symptom in each network was identified based on the highest centrality indices. Depression emerged as the core symptom in all networks, strongly associated with all fatigue dimensions (partial correlations ranging from 0.183 to 0.269) except mental fatigue. These findings indicate robust connections between symptoms, as variations in depression scores directly or indirectly influence fatigue and other symptoms within the cluster. Our results support the multidimensional aspect of CRF, and its links with other common symptoms. To effectively reduce patient CRF, interventions should address not only fatigue but also the closely related symptoms from the cluster, such as depression, given its centrality in the cluster. ClinicalTrials.gov (NCT03144154 and NCT04873661). Retrospectively registered on May 1, 2017 and April 29, 2021, respectively.

Assessing the dimensions and structure of thwarted interpersonal needs

IntroductionThe Interpersonal Needs Questionnaire-15 (INQ-15) assesses interpersonal needs comprising suicidal desire based on the Interpersonal Theory of Suicide (IPTS): Thwarted belongingness (TB) and perceived burdensomeness (PB). The IPTS is applied to many psychological-affective states and disorders, collectively showing state-specific pathways to suicidal features. Given the prevalent application of the IPTS, determining how the constructs are comprised and operate is critical. Consistent with the network perspective of psychological constructs, we used network and algorithmic methods to evaluate thwarted interpersonal needs. MethodsA sample (N = 863) aggregated from two independent studies (Sokol & Serper, 2019; Sokol et al., 2023) was used for analysis. ResultsNetwork analysis identified three dimensions that were invariant by suicide-risk status: PB, and two dimensions from the TB items. Accounting for wording effects, one-dimensional structures presented in 9 %–23 % of replicates. The loadings comparison test algorithm suggested that the constructs were consistent with a network composition (over latent), and centrality indices identified meaningful strong and bridge features. LimitationsThe samples were demographically homogeneous, and more diverse samples in terms of sexuality, racial/ethnic identification, culture, and socioeconomic status might yield different findings. ConclusionFindings indicate that thwarted interpersonal needs are comprised of perceived burdensomeness, loneliness, and absence of reciprocal care dimensions and that loneliness-related experiences may be mechanisms by which PB and TB-related experiences co-occur. Additionally, results showed that the INQ-15 may have previously unquantified method effects, but the overall identified dimensions are stable. These results yield a nuanced conceptualization of interpersonal needs and their function.

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data

ObjectiveThis study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis. MethodsTwenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups. ResultsAmong 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA. ConclusionsThe application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.

Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS / Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS

This study aims to analyze the connectivity of non-containerized ports, particularly in the sub-commodity of vegetable/animal oils and fats, within the operational area of PT Pelindo Multi Terminal. Using Social Network Analysis (SNA) and Geographic Information System (GIS) techniques, this study builds a network based on nodes (ports) and assesses inter-node connections with metrics such as degree centrality, betweenness centrality, and hub index. Gephi application was used for the calculation of network metrics, while QGIS visualized the shipping routes. The research revealed that high traffic ports such as Belawan, Dumai, and Tanjung Priok play a crucial role in the network. The findings support recommendations for infrastructure development and implementation of an integrated Terminal Operations System (TOS) to improve logistics efficiency.

Effectiveness of spiritual and religious interventions in patients with cardiovascular diseases: A systematic review and meta-analysis of randomized controlled trials.

This systematic review and meta-analysis aimed to combine the physiological, psychological, and spiritual health effects of spiritual and religious interventions on cardiovascular disease patients. Studies from January 1999 to December 2022 were sourced from ScienceDirect, PubMed, Web of Science, Scopus, Cochrane CENTRAL, and cumulative index to nursing and allied health literature databases. This meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Data were analyzed using Comprehensive Meta-Analysis Version 4. Heterogeneity was assessed using 95% confidence intervals (CIs), p values, and fixed or random-effects models. This systematic review included 15 studies with 2,047 participants, while the meta-analysis focused on 12 studies. Spiritual and religion-based interventions notably reduced participants' heart rate (g = -0.287, 95% CI = [-0.552, -0.021]), anxiety (g = -1.081, 95% CI [-1.364, -0.831]), and depression (g = -1.346, 95% CI [-1.965, -0.726]). Moreover, these interventions significantly enhanced spiritual well-being (g = 0.692, 95% CI [0.459, 0.926]) and hope levels (g = 1.152, 95% CI [0.347, 1.956]) compared to control groups. This meta-analysis flashes on utilizing spiritual and religion-based interventions for nurses, chaplains, psychologists, social workers, and other professionals who might lead patient care to enhance health outcomes for cardiac patients. (PsycInfo Database Record (c) 2024 APA, all rights reserved).