Chart Information Research Articles

Evaluating SLIM-based human error probability for ECDIS cybersecurity in maritime

AbstractThere is an undeniable recognition that maritime cybersecurity risk management should involve process, technology, and people. However, thus far, most studies have focused on the technical and process aspects of maritime cybersecurity, more than on the human element. On a vessel, the Electronic Chart Display and Information System (ECDIS) is, amongst all the electronic devices on the bridge, a complex and indispensable maritime sociotechnical system that must consider both technical and human aspects. In the context of maritime cyber resilience, it is important to note that when developing strategies for maritime cybersecurity, one cannot only consider technical security measures and ignore human error, as this does not adhere to good cybersecurity practice. To address this, this study aims to identify the navigating officers’ responsibilities for ECDIS cybersecurity and find the human error probabilities during these tasks via the SLIM-based human reliability analysis method. The outputs of this study provide an insight for industrial policies and best practices, in ECDIS cybersecurity risk management in terms of the behavioural and cultural aspects of shipping.

Investigating monotherapy and combination therapy trends in newly diagnosed patients with pulmonary arterial hypertension among EU3 & UK physicians over the COVID-19 pandemic

Abstract Background Pulmonary Arterial Hypertension (PAH) is a form of pulmonary hypertension, where the narrowing of arteries in the lungs restricts blood flow and so increases pressure in the vessels. Studies have demonstrated that initial combination therapies are optimal for PAH management. However, prescription of monotherapy treatment is still prevalent as a first line therapy. Purpose The purpose of this research was to investigate prescribing trends of physicians for first line patients with PAH in the UK, Germany, Italy and Spain. We investigated the proportions of newly diagnosed patients and the prescription trends for monotherapy and combination therapy prior to and during the COVID-19 pandemic. Methods A multi-country, multi-centre online medical chart review study of patients with PAH was conducted between April – June of 2019, 2020 and 2021 respectively. Recruited from a large access panel, 178 treating cardiologists, pulmonologists & rheumatologists in the UK (n=16), Germany (n=55), Italy (n=55) and Spain (n=52) were screened for duration of practice in their speciality and caseload (≥5 PAH patients in the last 3 months), and provided data on 694 PAH patients (UK = 71, Germany = 206, Italy = 208, Spain = 209). Reported patient data pertained to medical chart information reflecting the prior year, i.e., Q2 2021 data reflected the 2020 period (advent of the COVID-19 pandemic). Results In this dataset, there has been a consistent decrease in the proportion of newly diagnosed (i.e. diagnosed within 12 months of being reported) patients reported from 2019 to 2020 and 2021. In 2019, 49% of the reported patients were diagnosed within the last 12 months. However, the newly diagnosed patient population dropped to 37% in 2020 and continued to drop to 27% in 2021. Despite this, there has been an increase in reported first line patients within the newly diagnosed segment from 74% in 2019, to 75% in 2020, then at 87% in 2021. This increase can be seen to coincide with the ongoing COVID-19 pandemic. In 2019, 58% of reported newly diagnosed patients were recorded as receiving monotherapy. This did drop to 33% in 2020; however, in 2021 monotherapy uptake increased to 47%. Of note, the usage of the endothelin receptor antagonist (ERA) drug class increased from 67% in 2019 to 83% in 2020 but dropped to 69% in 2021. Conclusions This data set suggests a decreasing trend in newly diagnosed patients and a gradual shift in treatment type to first line monotherapy prescription, which coincided with the height of the COVID-19 pandemic. More newly diagnosed patients (those diagnosed within 12 months of being reported) are receiving monotherapy treatment at the expense of combination therapy, and this has also coincided with the pandemic. Further investigation using comparator cohort is warranted to assess whether the challenges physicians faced during the pandemic has had a causal effect on the prescribing habits for PAH therapies. Funding Acknowledgement Type of funding sources: None.

RedNavi: Building a 3D Scene of the Current Sea from AIS Data

The Automatic Identification System (AIS) is a kind of navigation equipment that exchanges a wealth of essential information among vessels and between ships to shore through Very High Frequency. Currently, identification and other navigational information can be obtained in real time with AIS data integrated into other shipborne systems, such as the Electronic Chart Display and Information System and radar. However, at present, AIS information is represented in a two-dimensional (2D) way, which is not the same as the three-dimensional (3D) world people perceive visually. In this paper, we introduce RedNavi, a sustainable computer 3D scene building system that visualizes the current sea, specifically the environment and traffic conditions around the ownship, using received AIS data. RedNavi has a wide range of application scenarios. Applying to the maritime education and training field, it can serve as a bridge between the 2D and 3D worlds, helping less experienced trainees build up their capabilities. Applying to actual navigation, it can provide the deck officer with another visual aid to their lookout in addition to existing 2D information systems. In addition, given the microservices architecture RedNavi adopts, the development, deployment, and maintenance processes become relatively lighter, faster, and easier, and therefore more sustainable than traditional monolithic systems.

Application Analysis of Overlapping Community Detection Algorithms for Multidimensional Network Big Data and IoT

Capture the design and elements of these layers. Each layer corresponds to an alternative connection type between hubs in the normal world and requires tracking down communities in multidimensional networks. Most community disclosure approaches for multidimensional networks, then again, may ignore the transaction between layers or a layer’s unmistakable topological construction. Moreover, most of them are just equipped for distinguishing nonoverlapping communities. In this exploration, we offer another multidimensional network community disclosure strategy that exploits the connection among layers and the extraordinary geography of each layer to track down overlapping communities. First, use an overall assessment of edge behavior within and between layers to calculate the similarity of edges from similar layers and cross layers. You can then use these similarities to build a dendrogram of a multidimensional network that takes into account both characteristic topology structures and basic transactions. Finally, you can remove the overlapping communities in these layers by splitting the dendrogram and adding another community thickness metric for the multidimensional network. We show that our strategy is precise in recognizing overlapping communities in multidimensional networks by applying it to both manufactured and genuine world datasets. In chart and enormous information examination, community detection is a commonplace issue. It involves finding groups of firmly associated hubs with little associations with hubs outside the bunch. Distinguishing communities in huge scope networks, specifically, is a basic errand in numerous logical fields. In the writing, community detection techniques have been demonstrated to be wasteful, bringing about the improvement of communities with uproarious communications. To defeat this requirement, a framework that decides the best community among multifaceted networks in light of important determination standards and substance dimensionality should be created, eliminating loud communications in a continuous setting. Our outcomes likewise show that it is vital to utilize integral measurements to assess the exhibition of overlapping community detection calculations. Performance metrics, such as the NMI or the Omega Index, only measure the overall quality of a detected cover, whereas complementary metrics give us more information about the behavior of each algorithm in detecting overlapping communities. Finally, while some algorithms perform well on synthetic networks, none of the algorithms can detect the community structure in real networks. This is due to the detected communities of the algorithms being substantially different from the communities defined by the meta-data.

Investigating periodontal diagnosis and treatment at one dental school.

The purpose of this study was to evaluate whether patients at one dental education institution received appropriate care based on their periodontal diagnosis in a timely manner. This study used a retrospective design to evaluate data from July 2018 to February 2020. The patient records were screened to determine whether patients received a periodontal diagnosis, whether the diagnosis followed the 2018 AAP Classification Guidelines, and if appropriate preventive and therapeutic procedures were completed in a timely manner. In addition, the type or year of dental student providing the procedure was also recorded. Data were analysed using descriptive statistics and a Chi-squared test. A total of 612 charts were generated for review and 157 met the inclusion criteria and were evaluated. Results revealed that more than half (56.7%) of the patient records did not demonstrate a periodontal diagnosis and another 10.8% did not follow current AAP Classification Guidelines. Most patients (n=125, 79.6%) had a comprehensive periodontal assessment performed, while 32 (20.4%) had no comprehensive periodontal charting information recorded. Further, of the 157 records reviewed, 96 (61.1%) had no periodontal treatment specified. A statistically significant difference with a modest association was found between type of dental student and year based on periodontal diagnosis (Fisher's exact test value=20.72, p=.001, Cramer's V=0.25). Documentation of key clinical information, diagnostic conclusions, and treatment rendered requires curriculum review. Further studies are warranted to determine whether similar findings exist among other dental school education programs.

Mapping evidence of Iran diabetes research: protocol for a scoping review.

A considerable amount of research funding goes to diabetes management strategies to improve therapeutic goals and reduce the burden of diabetes. A vast amount of the budget is wasted due to unnecessary studies. A scoping review is a pivotal study to overview the available evidence and avoid research waste. In this review, we will try to find out the scope of available studies on diabetes management interventions, identify associated research gaps, and prioritize future studies. We will carry out a study using Arksey and O'Malley's scoping review framework. We will search the Scopus and PubMed databases from 01/01/2015 till 01/01/2020. Only original articles related to pharmacological and non-pharmacological management interventions will be included. These interventional studies should be conducted on the Iranian population. After data extraction, a descriptive data analysis will be used to present information in different charts or tables. We will evaluate related published articles based on their document type, level of evidence, type of diabetes, subject area, interventions types, main findings and outcomes. This study represents the first attempt to sum up available studies related to diabetes management interventions performed in Iran. The results of this study will be useful for all the stakeholders and policy-makers involved in diabetes research. It can help clinicians to be informed about studies on management interventions and can guide scientists eager to diabetes research to choose their future research plans based on diabetes research requirements and gaps.

Situational awareness of navigators in high-speed craft bridge navigation operations

ABSTRACT Navigation is a joint operation between humans and technology. Navigating high-speed craft (HSC) is a more critical and risky task than other types of ship navigation due to high speed and time limitations. Situational awareness (SA) is the understanding of what is going on around the navigators. HSC navigator who has an insufficient level of SA may lead to wrong decisions and even accidents. In this study, an analysis of Turkish HSC navigators’ SA levels has been carried out in their working environment. Situational Awareness Rating Technique (SART) and pairwise comparison of the effects of Electronic Chart Display and Information System (ECDIS), Automatic Radar Plotting Aid (ARPA), Automatic Identification System (AIS) and autopilot on SA questionnaires were applied. According to SA, navigation operations, factors that mostly affect SA and suggestions that would increase SA were sorted in accordance with the navigators’ perceptions. The results show that the level of HSC navigators’ SA is high and sufficient. Finally, considerations are introduced to keep acceptable levels or increase SA on HSCs.

Safety culture of navigators in terms of bridge operations on high-speed crafts

ABSTRACT Maritime safety is important for all stakeholders. On the other hand, maritime accidents occur despite rapid advances in technology. One of the most important aspects necessary to realise safety at sea is the safety of navigation. Navigating high-speed craft (HSC) is a more challenging and risky task than other types of ship navigation. A HSC navigator with insufficient safety culture (SC) may lead to wrong decisions in challenging physiological and psychological conditions. In this study, an analysis of HSC navigators’ SC in terms of bridge operations has been carried out. Nordic Occupational Safety Climate Questionnaire (NOSACQ-50) and pairwise comparison of the effects of Electronic Chart Display and Information System (ECDIS), Automatic Radar Plotting Aid (ARPA), Automatic Identification System (AIS) and autopilot on SC have been applied to 38 Turkish HSC navigators. Navigation operations according to SC, factors that mostly influence SC and suggestions that will improve SC have been identified and discussed in accordance with navigators’ perceptions. The results show that the overall average level of HSC navigators’ SC is low. Therefore, considerations are introduced to improve SC in HSCs.

Chart GCN: Learning chart information with a graph convolutional network for stock movement prediction

Advanced deep learning methods have been widely adopted in stock movement prediction with technical analysis (TA), while researchers prefer technical indicators to technical charts due to the divergence in quantification difficulty. In traditional TA, researchers usually utilize chart similarity to solve the quantifying problem, while chart similarity is often limited to specific charts as the templates for comparison, resulting in massive inadequate use of information. Accordingly, we propose a novel similarity framework to overcome the limitation of chart similarity to specific charts. Specifically, after extracting the key point sequence (i.e., the draft chart) from the stock price series, we transform it into a graph and ultimately employ an arbitrary graph kernel such as the Weisfeiler-Lehman graph kernel and graph convolutional network (GCN) to sufficiently mine the information in the chart for stock movement prediction. Our similarity framework is more robust than the chart similarity measures commonly used in traditional TA. Additionally, we further evaluate the effectiveness of our framework on real-world stock data and show that our framework achieves the best performance compared to several state-of-the-art baselines in stock movement prediction and obtains the highest average net values in a trading simulation. Our results complement the existing application of the chart similarity method in deep learning and provide support for the investing application of financial market decisions.

Logical grouping of data and control functions on the displays of shipboard navigation systems

AbstractStandards IEC 62288:2014 and MSC.191(79) require information on the displays of shipboard navigation systems to be logically grouped, but only provide limited specification for this ‘logical’ criterion. Meanwhile, complex interfaces and information overload remain as major design issues, being connected to several maritime accidents. To address this matter, a three-phase study was conducted to develop a pattern to organise essential information on Radar and Electronic Chart Display and Information System (ECDIS) displays and their equivalent modules on integrated navigation systems and integrated bridge systems. The first phase involved identifying the information most essential for safe navigation using cognitive task analyses, equipment performance standards and frequency of use. The second phase involved a card-sorting experiment with seafarers (n = 63) to develop an initial grouping pattern for the identified essential information. The third phase involved validating the initial grouping pattern with a new sample of seafarers (n = 35). The result is a pattern to group 48 types of information on shipboard navigation displays into 13 groups. The paper details the selected methods and the findings, and provides implications for future research.

Care ethics framework for midwifery practice: A scoping review.

Background: As a normative theory, care ethics has become widely theorized and accepted. However, there remains a lack of clarity in relation to its use in practice, and a care ethics framework for practice. Maternity care is fraught with ethical issues and care ethics may provide an avenue to enhance ethical sensitivity.Aim: The purpose of this scoping review is to determine how care ethics is used amongst health professions, and to collate the information in data charts to create a care ethics framework and definition for midwifery practice.Method: The scoping review was conducted according to the Preferred Reporting Items for Scoping reviews (PRISMA-ScR) and Joanna Briggs Institute (JBI) recommendations. The search was applied to the databases CINAHL, MEDLINE, PschInfo and Pubmed which were searched in September 2019 and again in July 2021. The inclusion criteria were guided by the mnemonic for search terms: Participants, Concept, and Context (PCC) and included variations of health care professionals, care ethics and utilization. The search was limited to qualitative studies published in English between 2010 and 2021. A data extraction tool was used to extract and synthesize data into categories. The articles were screened for eligibility by title, abstract and full text review, by two independent reviewers.Ethical Considerations: The scoping review was guided by ethical conduct respecting authorship and referencing sources.Results: Twelve of the initially identified 129 studies were included in the scoping review. Data synthesis yielded four categories of care ethics use by health professionals: relationship, context, attention to power and caring practices. In combination, the evidence forms a framework for care ethics use in midwifery practice.Conclusion: Care ethics use by health professionals enhances ethical sensitivity. A framework and definition for care ethics for midwifery practice is proposed. This review will be of interest to midwives and other health practitioners seeking to enhance ethical sensitivity.

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

BlueNavi: A Microservices Architecture-Styled Platform Providing Maritime Information

Traditional methods of marine navigation are undergoing a revolution brought about by the almost universal adoption of the Automatic Identification System (AIS). AIS exchanges a wealth of navigational information among vessels and between ships to shore through Very High Frequency (VHF). With AIS data integrated into the Electronic Chart Display and Information System (ECDIS), the identification and navigational information of surrounding vessels as well as aids to navigation can be reflected on the electronic charts in real time, despite some problems such as the low AIS carriage rate on small vessels where it is not mandatory and the high cost of ECDIS preventing such vessels from installing it. In this paper, we introduce BlueNavi, a lower cost but sustainable maritime information providing platform built with microservices architecture allowing flexible on-demand scalability and cross-platform adaptability. Applications served by BlueNavi can provide users with data either stored in a remote data center through the internet or received locally by devices connected to the station without the need for the internet. From our land test, we show that users with only an internet connection but without any AIS equipment can also obtain live AIS data collected by other stations. Conversely, with access to the internet, BlueNavi can also send data back to the land stations, enabling other ships to identify non-AIS ships as well. Through the live-ship test, we demonstrate that BlueNavi works well offline in cooperation with shipborne AIS equipment. We also look at some possible application scenarios for BlueNavi with other data sources and means of communication other than AIS and VHF that can be expanded to the platform. BlueNavi will enable inexpensive ship identification for small vessels and provide an extension of functionality to ECDIS for large ships.

Cyber security risk assessment in autonomous shipping

Autonomous ships would require higher cyber-physical interaction in comparison with traditional shipping operations, thus increasing the vulnerabilities associated with cyber security. The increasing complexity surrounding the innate characteristics of the shipping industry makes it challenging to build a resilient framework for ensuring cyber security. This study proposes a multi-criteria decision-making (MCDM) framework for assessing cyber security risk in the autonomous shipping context. The research was validated through surveying subject matter experts, system designers and seafarers. Different types of equipment and systems are ranked based on their perceived vulnerability to cyber threats. Survey data from 28 subject matter experts are collected and analysed through the Bayesian best–worst method (BWM). At system level, the results indicate that navigational systems are the most vulnerable to potential cyber threats, while propulsion systems are the least vulnerable element in the context of future autonomous shipping operations. On a sub-system level, the three most vulnerable parts are Global Navigation Satellite System (GNSS), Electronic Chart Display and Information System (ECDIS) and the communication devices on shore control centres (SCC), while the least vulnerable parts are engine controls, SCC integration platforms and cargo handling at ports.

Efficiency Comparison between Audible and Buzzle Alarms of Electronic Chart Display and Information System Alarm under the Simulated Environment

Along with the development of ship navigational equipment, ship operators have to process a larger amount of information than before and be exposed to more alarm sounds. These ships’ bridge environment increases burdens to ship operators. One of the methods proposed to solve this problem is the audible voice alarm method. However, there is a lack of studies that objectively prove the efficacy of the method. Therefore, in this study, a comparative experiment was performed to confirm the effect by applying the method to an electronic chart display and information system (ECDIS), a representative navigation instrument. We analyzed collected data according to a data-driven process and confirmed the difference between a traditional alarm method and the audible voice alarm method by distinguishing groups through clustering.

Membership Inference Attacks on Machine Learning Models : A Review

Ongoing investigations propose enrollment derivation (MI) assaults on profound models, where the objective is to surmise if an example has been utilized in the preparation interaction. Regardless of their obvious achievement, these examinations just report exactness, accuracy, and review of the positive class (part class). Subsequently, the presentations of these assaults have not been plainly covered negative class (non-part class). AI (ML) models have been broadly applied to different applications, including picture grouping, text age, sound acknowledgment, and chart information examination. Nonetheless, late investigations have shown that ML models are helpless against participation induction assaults (MIAs), which mean to gather whether an information record was utilized to prepare an objective model or not. MIAs on ML models can straightforwardly prompt a security break. For model, through distinguishing the way that a clinical record that has been utilized to prepare a model related with a specific infection, an assailant can surmise that the proprietor of the clinical record has the sickness with a high possibility. As of late, MIAs have been demonstrated to be compelling on different ML models, e.g., arrangement models and generative models. In the interim, numerous safeguard strategies have been proposed to relieve MIAs.

Modeling the impact of the River Information Services Directive on the Performance of inland navigation in the ARA Rhine Region

River Information Services (RIS) are harmonized digital services that support safety, transport and traffic management in the inland navigation. RIS refer mainly to four key technologies such as Vessel Tracking and Tracing (VTT) with onboard Inland Automatic Identification System transponders (Inland AIS), Electronic Reporting International (ERI), Notices to Skipper (NtS) and Inland Electronic Chart Display and Information System (Inland ECDIS). These key technologies were developed during the nineties and received in Europe a regulatory framework through the Directive EC/2005/44 (the RIS-Directive). In 2009, most EU Member States had implemented the Directive. One of the claimed benefits of the implementation of RIS relates to a growing performance of inland navigation. In this paper, the impact of RIS on the performance of inland navigation is analyzed via a structural break cointegration approach, followed by an Error Correction Model (ECM) estimation. The empirical analysis shows that the performance of the dry bulk market is highly dynamic, while the liquid bulk market is moderately responsive to shocks. The structural break analysis could not capture any effect of the Directive on the performance of both markets, which can be attributed to several factors, such as the difficulty to isolate the policy impact, other drivers of variation, interferences of other policies and changes at the demand side.

A critical analysis of chromotherapy (color therapy) and its impact on married life

Background: Chromo therapy commonly known as color therapy falls under the category of Complementary and Alternative Medicine System (CAMS) treatment by utilizing electromagnetic radiations with different frequencies which affect human neurohormonal pathways and play a role in day-to-day life. It is one of the most successful ancient practices which are now gaining interest as valid and effective science. It is a well-established fact that chromo therapy triggers specific points in our body and affects various parts of life such as married life significantly. With this in mind, we conducted a survey to assess the effect of chromo therapy on married life. 500 participants were chosen for the study and after including various colors suggested to them in their life, an improvement in various aspects of their life was observed. Aim: To assess the effect of chromo therapy on the life of couples married for at least two to three years. Methodology: About 500 couples participated in a survey we formulated exclusively for this research. The criteria for participation were to be married for at least 2 years, starting from the date of the wedding. The questionnaire included each individual’s astrological chart information to draw an accurate conclusion. Results: All the calculations were made based on keeping into consideration each couple’s numerological and astrological charts. We found that 90% of couples who face issues in their married life failed to choose their lucky color at their wedding venues. Furthermore, 70% of the respondents did not select the right day of marriage as per their charts, and thus faced problems. Conclusion: Our Study revealed selecting the right color in life events such as marriage can have a positive impact on different facets of life.

GNSS Independent Position Fixing using Multiple Navigational Features Registration

Safe navigation at sea depends on reliable position information. The easy to use and readily available navigation satellite systems are providing round the clock position information for seafarers, but denial of these services by jamming or spoofing techniques have become frequent, and other means are needed to validate critical navigation information. This paper revisits traditional methods to estimate position and heading of a vessel and offers a framework for computerized calculation of these basic observables without the use of radio-borne external information. Using radar and sea chart information, the paper investigates the quality of navigation information based on observed location of beacons, landmarks, coastlines and buoys. The paper demonstrates convincing accuracy in both position and heading estimation.

Long-term Effects of a Brief Mindfulness Intervention Versus a Health Enhancement Program for Treating Depression and Anxiety in Patients Undergoing Hemodialysis: A Randomized Controlled Trial

Background:Depression and anxiety affect approximately 50% of patients with kidney failure receiving hemodialysis and are associated with decreased quality of life and increased risk of hospitalization and mortality. A Brief Mindfulness Intervention (BMI) may be promising in treating depressive and anxiety symptoms in this population, but the long-term sustainability of the intervention’s effects is unknown.Objective:We previously conducted a randomized controlled trial (RCT; n = 55) comparing an 8-week BMI with an active control (Health Enhancement Program [HEP]) for patients receiving dialysis, with depression and/or anxiety. Here, we examine the 6-month follow-up data to determine the long-term sustainability of BMI versus HEP in reducing (1) depressive symptoms, (2) anxiety symptoms, and (3) the efficacy of BMI versus HEP in reducing the likelihood of hospitalization.Design:In this study, we analyzed 6-month follow-up data from an 8-week assessor-blinded parallel RCT, which evaluated the efficacy of a BMI against an active control, HEP, in patients receiving hemodialysis with symptoms of depression and/or anxiety.Setting:The study took place at hemodialysis centers in 4 tertiary-care hospitals in Montreal, Canada.Participants:Participants included adults aged ≥18 years who were receiving in-center hemodialysis 3 times per week and had symptoms of depression and/or anxiety as indicated by a score ≥6 on the Patient Health Questionnaire–9 (PHQ-9) and/or the General Anxiety Disorder–7 (GAD-7).Methods:Participants were randomized to the treatment arm (BMI) or the active control arm (HEP) and completed assessments at baseline, 8 weeks, and 6-month follow-up. Depression was assessed using the PHQ-9, and anxiety was assessed by the GAD-7. Hospitalization rates were assessed using medical chart information.Results:We observed significant decrease in depression scores over 6 months in both BMI and HEP groups, with no significant difference between groups. Anxiety scores significantly decreased over 6 months, but only in the BMI group. Brief Mindfulness Intervention and Health Enhancement Program were comparable in terms of hospitalization rates.Limitations:The limitations of our study include the modest sample size and lack of a third arm such as a waitlist control.Conclusions:Our results suggest that the beneficial effects of BMI and HEP for improving mood disorder symptoms in patients receiving dialysis persist at 6-month follow-up. Both interventions showed sustained effects for depressive symptoms, but BMI may be more useful in this population given its efficacy in reducing anxiety symptoms as well.Trial registration:Prior to recruitment, the trial had been registered (ClinicalTrials.gov Identifier: NCT03406845).