Software Maintenance Research Articles

The effectiveness of neuromuscular training warm-up program for injury prevention in adolescent male basketball players

ABSTRACT This study evaluated the effects of a neuromuscular training (NMT) warm-up program on injury incidence, neuromuscular function, and program adherence, maintenance and acceptance in adolescent basketball players. A total of 275 players from 20 Slovenian teams (15 ± 1.7 years of age), were randomized into an intervention group (IG, n=129) and a control group (CG, n=146). Over three months, the IG incorporated NMT into their warm-ups, while the CG followed their usual practice. Measurements of body anthropometry, muscle contractile properties, and balance were taken before and after the intervention. Also, the injury incidence, training adherence and maintenance were reported. Both groups showed improved balance, with no significant difference between them. However, IG demonstrated reduced delay times in specific muscles, indicating improved neuromuscular function. Injury prevalence proportion (%) during the whole study period was higher in the control group compared to intervention (IG: 10.9% vs. CG: 23.3%), and incidence rate. Moreover, the incidence rate ratio for sustaining an injury was 2.6 on average (ranging from 0.88 to 7.07 for tendon and muscle injuries, respectively), indicating significantly lower injury risk in IG than CG. These findings highlight the effectiveness of NMT warm-ups in reducing injury risk and enhancing neuromuscular function, emphasizing the value of structured injury prevention strategies in youth sports..

Incorporation of software in the life cycle assessment of an ICT service: A case study of an ICT service for energy efficiency in the transport sector

AbstractInformation communication and technology (ICT) services and solutions can improve resource efficiency in a variety of sector, but also result in direct environmental impacts. This study assesses the direct environmental impacts of an ICT service that improves vehicle fuel efficiency using a cradle‐to‐grave life cycle assessment (LCA). This is one of the first studies to examine the entire life cycle of an ICT service from development to use and maintenance, with a focus on software—an aspect that is typically neglected in previous studies. The results suggest that software development and maintenance and the use of in‐vehicle communicators for data transmission have the largest environmental impacts across multiple categories. Deployed across a fleet of 150,000 vehicles over 5 years, we estimate that the ICT service is responsible for 174 tCO2e. However, this is negligible compared with the total emissions of the fleet and the potential savings from the service, given a single diesel vehicle in this fleet emits around 130 tCO2e over the same period. We explore several scenarios to reduce the footprint of the ICT service. The largest potential reduction of around one‐third is achieved by replacing in‐house servers with cloud computing in a data center located in a region with low‐carbon electricity. The study demonstrates how LCA can be used to assess the environmental impacts of ICT services and the importance of considering software in these assessments.

From Software Management to Software Ecosystem Governance: A Bibliographic Review on Software Governance Practices Within Organizations and Their Evolution

Objective: The objective of this study is to map the publications that deal with IT Governance and the main aspects of Software development, acquisition and maintenance, identifying when the concepts related to software governance emerged and the main search sources for publications on the subject, highlighting the central issues of each of the emerging contexts. Theoretical Framework: A theoretical framework was used on the organizational structure of IT governance areas, focused on directing, analyzing and monitoring software development, differentiating the typical management activities designed by Juiz and Colomo-Palacios (2020). Method: The methodology adopted for this research comprises a qualitative-quantitative approach, using bibliometrics, bibliographic analysis and the application of a fractional authorship algorithm to identify the main authors to publish on the topic. Data collection was done using a set of search databases such as the Web of Science database and the Scopus database. Results and Discussion: The results obtained revealed the main authors identified in each of the databases searched, as well as the central themes of the searched themes, indicating the first appearance of each term and its main article. It is also possible to analyze the academic production related to each of these themes. Research Implications: The practical and theoretical implications of this research indicate fields of research that have become popular, bringing perspectives for future work, as well as fields that are being poorly explored in software development governance and management. Originality/Value: This study contributes to the area of ​​Information Technology management as the main themes studied in governance and management of software development formalize essential concepts for these fields of research and promote process improvement.

Examining the Accuracy of Self-Reported Antiretroviral Therapy Adherence Using a Visual Analog Scale Among Opioid-Dependent People Living with HIV.

Evidence indicates that regular assessment of antiretroviral therapy (ART) adherence is necessary to promote adherence and achieve viral suppression. Self-reported adherence using a visual analog scale (VAS) has been used extensively to measure ART adherence. However, less is known about the accuracy of the VAS for measuring ART adherence among opioid-dependent people living with HIV. In this study, we aimed to evaluate the accuracy of the VAS in measuring ART adherence in opioid-dependent people living with HIV who are enrolled in a methadone maintenance program (MMP). This study was conducted within a larger randomized controlled trial among opioid-dependent people living with HIV (N = 109) who were enrolled in an inner-city MMP. Self-reported VAS ART adherence data were compared to pharmacy refill data, which is a more objective measure. Self-reported VAS ratings and pharmacy refill behavior were significantly correlated at the 6-month post-intervention mark (r = 0.349, p = 0.006) but not at any other time point (i.e., pre-intervention, post-intervention, 3-months, and 9-months post-intervention). Based on the pharmacy refill data, participants did not consistently adhere to their ART medication but self-reported high adherence on the VAS. The self-report VAS may not be an accurate measure for assessing ART adherence among opioid-dependent persons living with HIV and enrolled in a MMP. Based on our findings, it may be more accurate to use pharmacy refill information or other objective measures to quantify ART adherence over extended periods for this patient population.

Deep Configuration Performance Learning: A Systematic Survey and Taxonomy

Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled without having a thorough knowledge of the software system, but relying mainly on data, which fits precisely with the purpose of deep learning. In this paper, we conduct a comprehensive review exclusively on the topic of deep learning for performance learning of configurable software, covering 1,206 searched papers spanning six indexing services, based on which 99 primary papers were extracted and analyzed. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data, the construction of deep learning performance models, the evaluation of these models, and their utilization in various software configuration-related tasks. We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field. To promote open science, all the raw results of this survey can be accessed at our repository: https://github.com/ideas-labo/DCPL-SLR .

Filtering Useful App Reviews Using Naïve Bayes—Which Naïve Bayes?

App reviews provide crucial feedback for software maintenance and evolution, but manually extracting useful reviews from vast volumes is time-consuming and challenging. This study investigates the effectiveness of six Naïve Bayes variants for automatically filtering useful app reviews. We evaluated these variants on datasets from five popular apps, comparing their performance in terms of accuracy, precision, recall, F-measure, and processing time. Our results show that Expectation Maximization-Multinomial Naïve Bayes with Laplace smoothing performed best overall, achieving up to 89.2% accuracy and 0.89 F-measure. Complement Naïve Bayes with Laplace smoothing demonstrated particular effectiveness for imbalanced datasets. Generally, incorporating Laplace smoothing and Expectation Maximization improved performance, albeit with increased processing time. This study also examined the impact of data imbalance on classification performance. Our findings suggest that these advanced Naïve Bayes variants hold promise for filtering useful app reviews, especially when dealing with limited labeled data or imbalanced datasets. This research contributes to the body of evidence around app review mining and provides insights for enhancing software maintenance and evolution processes.

Optimization of Maintenance Strategies in an Oil and Gas Production Facility to Minimize Maintenance Cost

Abstract: In this study, maintenance strategies in an oil and gas production facility were optimized to minimize maintenance costs. The centrifugal pump system in Port Harcourt Refinery was selected for this study because it significantly impacts on the productivity of the refining plant. The pump system’s failure mode effects and criticality analysis (FMECA) were examined, and an optimized maintenance task was developed for the system. The exponential reliability method was employed to analyse the pump’s failure data to determine the pump systems' failure rate and reliability while reliability centred maintenance (RCM) and linear programming (LP) model were employed to optimize the pump’s maintenance strategies and the pump’s maintenance labour force respectively. Broad-based results of the optimized maintenance strategies consisted of on-condition-directed (CD) maintenance, preventive maintenance (PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The optimized maintenance labour force result revealed that approximately three engineers and two technicians should be employed for the pump’s maintenance task with a minimum of N2, 585, 700 as cost of salaries for the labour force monthly as against the current maintenance labour force requiring fourteen engineers and thirteen technicians monthly. The results showed that the labour cost decreased from N180,000,000 per year to N31,028,400 per year (approximately 82.76% reduction) using the proposed optimized maintenance task compared to the current maintenance plan. Conclusion and recommendation were made that the maintenance optimization technique presented in this work should be adopted by the oil and gas processing and production companies in order to minimize maintenance cost.

Generative Models for Source Code: Fine-Tuning Techniques for Structured Pattern Learning

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality.

Improved machine learning models with a similarity-based approach for remaining useful life prediction

Abstract Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment. In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified. We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Mechanisms that enhance Internet of Things engagement

Purpose By drawing on previous research on mechanism-based explanations and business-to-business engagement, the purpose of this study is to identify and define mechanisms that enhance Internet of Things (IoT) engagement. Design/methodology/approach By positioning the study within the paradigm of critical realism (CR), this paper used multiple case study research. This paper applied 12 in-depth, semi-structured interviews, an observation and firm documents as data-gathering tools. Findings This paper argues that the higher-level phenomenon (Institutional logic of the IoT ecosystem) leads to a higher-level outcome (IoT engagement). As lower-level situational mechanisms, this paper found IoT readiness and transparency in the ecosystem. Action-formation mechanisms were acknowledged as communication, availability of an IoT interface, and support. Commitment, trust, satisfaction and software maintenance and updates were recognized as transformational mechanisms. Practical implications The findings will help managers to understand which mechanisms to focus on when forming engagement strategies for onboarding new actors and strengthening relationships with existing actors. Furthermore, this paper suggests considering the IoT readiness of new actors more critically, as this mechanism was found to be the most crucial one for an early stage of engagement in an IoT ecosystem. Originality/value This study helps understand the causal structures behind engagement and enhances the theoretical and practical domain of IoT engagement. In addition, this study demonstrates the value of applying CR for generating knowledge about a phenomenon through causal explanations.

Tobacco Cessation: Screening and Interventions.

Tobacco use has been the leading cause of disease and death in South Korea. Early detection of tobacco use and evidence-based interventions play pivotal roles in facilitating tobacco cessation. In accordance with the earlier iterations of the Lifetime Health Maintenance Program (2009) and recent recommendations from the United States Preventive Services Task Force (USPSTF; 2021), two themes were chosen for investigation: the identification of and intervention for tobacco use. The USPSTF recommendations were formulated by conducting an overview of reviews. In this study, literature searches and quality assessments of reviews were conducted. The findings highlighted the efficacy of physician-led identification and advising in promoting tobacco cessation, with robust evidence supporting the implementation of behavioral and pharmacological interventions. These interventions significantly increased the likelihood of successful cessation compared with usual care. Digital interventions, such as internet- or mobile-based interventions, showed additive effects for quitting. Identification and targeted interventions are essential for tobacco cessation. By leveraging evidencebased strategies and enhancing access to resources, healthcare providers can empower individuals to achieve successful tobacco cessation and improve overall health outcomes.

Cybersecurity Maintenance in the Automotive Industry Challenges and Solutions: A Technology Adoption Approach

Numerous attempts have been made to create a secure system that meets the criteria and requirements of the automotive vehicle development life cycle. However, a critical gap exists in the secure development lifecycle, particularly concerning the development and maintenance of software after the vehicle has been sold by the manufacturer. This step is often overlooked by original equipment manufacturers (OEMs), especially after the expiration of the vehicle warranty period, given the cost that it will require to update and test the software in their vehicles. This paper addresses the issues that affect current and future vehicle cybersecurity, during the maintenance of cybersecurity, and how the neglect of it could end up creating hazards for the vehicle owner or other road users. To accomplish this, we will employ the technology adoption model (TAM) as a theoretical framework, which is used to understand and predict how organizations adopt technology. Thus, through qualitative and quantitative research, including text mining, we identify the challenges in the adoption and diffusion of cybersecurity maintenance in the automotive sector and its supply chain. In addition, we propose possible solutions on how to maintain a level of security that will benefit road users, OEMs and regulators, covering the cybersecurity needs for the vehicle’s usable life, taking into account the vehicle’s heterogeneity of components and technology, connectivity, environmental impact and cost of production and maintenance of a vehicle.

Backyard flock sampling and artificial intelligence: A dual strategy for early detection of Avian Influenza and Newcastle Disease

Avian Influenza (AI) and Newcastle Disease (ND) are significant avian diseases that pose substantial threats to the commercial poultry industry due to their high contagion rates and potential for severe outcomes. Recent shifts in the epidemiology and ecology of both viruses have led to devastating impacts on wild bird and poultry populations, as well as farms and communities worldwide. These challenges underscore the necessity for One Health approaches to pandemic prevention and preparedness, emphasizing the need for multisectoral collaboration among animal, environmental, and public health sectors. To mitigate future pandemic risks, it is essential to control the transmission of AI and ND viruses among domestic and wild animals as well as humans. This involves addressing the upstream drivers of outbreaks, ensuring rapid response mechanisms, and conducting thorough risk assessments for zoonotic diseases. Effective implementation and maintenance of prevention programs, surveillance, and outbreak responses require strong political commitment and sustainable funding. A robust combination of active and passive surveillance strategies should be applied to monitor and control them. In addition, the integration of modern technologies, such as artificial intelligence and big data analytics, can enhance both active and passive surveillance efforts by improving the accuracy and speed of data collection, analysis, and reporting. The use of backyard flocks as an early warning system for avian diseases represents a proactive approach to understanding and managing the risks associated with AI and ND through targeted surveillance and timely interventions. In response to these challenges, the EpirORNIS Project aimed to leverage these sentinel backyard flocks for early detection of AI and ND, thereby enhancing surveillance and control strategies.

Empowering patients with heart failure: a digital health solution improved self-care and metabolic outcomes in a randomized controlled trial

Abstract Background/Introduction Heart failure is a common cause of morbidity and mortality and poses a significant burden on patients and healthcare providers. Purpose The aim of the study was to evaluate whether a digital health program improves quality of life and clinical outcomes for patients with heart failure. Methods A single-center, randomized controlled trial (RCT) was conducted with patients receiving standard of care (SoC) at a heart failure outpatient clinic. Patients were randomized (1:1) to receive either SoC alone or SoC with an additional six-month digital health program. The program consisted of remote patient monitoring (RPM) with symptom tracking, disease-specific education, and lifestyle coaching, designed to empower patients with heart failure. After the main program concluded, a six-month maintenance program was initiated, consisting of recurrent educational materials, with reduced coaching- and RPM frequency. The primary endpoint was the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12) at six months. Secondary endpoints included the European Heart Failure Self-care Behaviour Scale-9 (EHFScB-9), 6-question heart failure knowledge questionnaire, and metabolic outcomes. Here we report on the Intention-to-Treat (ITT) population analysis for the primary and significant secondary endpoints at six and 12 months. Results The ITT included 175 participants, 86 in the program group and 89 in the control. Program group: mean age (SD) 64.7 (13.2), 66.3% male, 87.2% with NYHA stages I-II. Control group: mean age (SD) 66.2 (11.1) years, 69.7% male, 87.6% with NYHA stages I-II. Median compliance with RPM was 93% and in-app retention was 80% at six months. Results for primary and secondary endpoints are presented in Table 1 and Figure 1. No statistical difference was observed in the KCCQ score between groups at six or 12 months. The program group had statistically significant improvements in EHFScB-9 score at six and 12 months and in heart failure knowledge at 12 months compared to the control group. Metabolic outcomes: a significant difference in glucose levels was also observed between groups at six months, and in HbA1c and triglycerides levels at 12 months. Conclusions The difference between groups in the primary endpoint of heart failure symptoms and quality of life, as measured by the KCCQ-12, was not statistically significant. However, significant differences were observed in self-care behaviour and heart failure knowledge, as well as metabolic outcomes at six and 12 months, when compared with standard care alone. These findings, along with good retention and engagement, indicate the value to patients of digital health interventions in the management of heart failure.

Lessons learned: strategies for implementing and the ongoing use of LI-RADS in your practice.

The establishment of the Liver Imaging Reporting and Data System (LI-RADS) in 2011 provided a comprehensive approach to standardized imaging, interpretation, and reporting of liver observations in patients diagnosed with or at risk for hepatocellular carcinoma (HCC). Each set of algorithms provides criteria pertinent to the various components of HCC management including surveillance, diagnosis, staging, and treatment response supported by a detailed lexicon of terms applicable to a wide range of liver imaging scenarios. Before its widespread adoption, the variability in the terminology of diagnostic criteria and definitions of imaging features led to significant challenges in patient management and made it difficult to replicate findings or apply them consistently. The integration of LI-RADS into the clinical setting has enhanced the efficiency and clarity of communication between radiologists, referring providers, and patients by employing a uniform language that averts miscommunications. LI-RADS has been strengthened with its integration into the American Association for Study of Liver Diseases practice guidelines. We will provide the background on the initial development of LI-RADS and reasons for development to serve as a starting point for conveying the system's benefits and evolution over the years. We will also suggest strategies for the implementation and maintenance of a LI-RADS program will be discussed.

Grounded Theory on Creating and Maintaining Adult Education Programs in Selected Higher Education Institutions in the Philippines

<p style="text-align:justify">This study is an attempt to develop a grounded theory on how adult education in higher education (AEHE hereafter) programs in the Philippines are created and maintained. The grounded theory illustrates the interrelation of factors that stimulate or serve as the drivers of AEHE in higher education institutions in the Philippines as well as the management and maintenance of AEHE at the operational and strategic levels, hence illustrating the bigger picture of adult education in higher education in the Philippines. Funded by the Commission on Higher Education (CHEd) of the Philippines, this study was part of a larger research project that investigated adult education in Philippine higher education institutions, that is, State Universities and Colleges (SUCs) and Private Higher Education Institutions (PHEIs). The data were derived from focus group discussions (FGD) with 43 participants from eight SUCs and nine private HEIs that have adult education programs that are regularly offered every academic year. The grounded theory drawn from the study pointed to the contextual – personal and institutional – factors, strategic and operational levels of management as well as the critical role of academic policy for the implementation and maintenance of AEHE programs, collaboration, and a compelling vision to ensure that adult education in higher education in the Philippines is prioritized in the country with involvement of relevant government agencies that will draw definite agendas and priorities to attain the goals of AEHE through a structure and management to carry out its functions and realize its compelling objective of creating a cadre of AEHE educators and specialists and adults suited for the demands of a knowledge-driven economy.</p>

Software Maintenance Cost Control Method in Software Engineering

This paper aims to explore the control method of software maintenance cost in the field of software engineering, combine theoretical knowledge and practical experience, analyze the factors affecting software maintenance cost in detail, and put forward the corresponding control strategy. Through literature review, case analysis and other methods, we reveal the importance of software maintenance cost control, and propose a series of concrete and feasible control measures, in order to provide reference for practitioners and students in the field of software engineering.

Maintenance Costs, Time, and Efforts Following Implant Therapy With Fixed Restorations Over an Observation Period of 10 Years: A Randomized Controlled Clinical Trial.

To assess the costs and efforts of maintenance therapy following implant treatment with fixed restoration over an observation period of 10 years. This randomized controlled clinical trial included 64 patients who were randomly assigned to receive one of two implant systems (AST or STM) and fixed restoration. Patients were included in a regular maintenance program and were examined at loading, 1, 3, 5, 8, and 10 years. Outcome measures included technical and biological complications, time, efforts, and costs to resolve them. A total of 97 implants were placed in 64 patients (AST: 54, STM: 43). Patient recall rates at 5 and 10 years were 89% and 67%. In general, technical complications were resolved within one to two appointments (mean = 1.5), and biological complications required a mean of 1.3 appointments. The overall regular maintenance time for the period of 10 years amounted to 77 min per year. Technical complications occurred in 39.5% of the patients, with screw-loosening being the most common one (43.4% of all complications). The most time-consuming technical complication was abutment fracture (94 min ± 68), followed by screw fracture (84 min ± 38). The prevalence of peri-implant mucositis on the patient level was 30.2%, and it was 9.3% for peri-implantitis. The average annual maintenance costs amounted to 9% of the initial cost of the implant treatment over the period of 10 years. Additional regular maintenance costs and costs due to the treatment of potential complications have to be taken into consideration when placing dental implants. The majority of technical complications could be resolved within one appointment, whereas the time needed to treat biological complications varied between one and three appointments for peri-implantitis.

FSECAM: A contextual thematic approach for linking feature to multi-level software architectural components

Linking software features to code components is commonly performed during software development and maintenance, including to implement a feature, document code, design test cases, trace requirements, track changes, and support inspection of safety–critical software by government and other third parties. However, manually mapping features to code is error-prone and time consuming, even for developers familiar with a system. To overcome these challenges several studies proposed automated techniques to reduce human intervention when linking features to code components. Nonetheless, three challenges remain: (i) accuracy, (ii) cost, and (iii) explainability. Linking of irrelevant code snippets causes an extra burden of analyses. If the approach lacks explainability, then a tool is less useful for many crucial systems such as safety–critical software. Moreover, heavyweight techniques such as those that require generating execution traces of every scenario or require training deep-learning models are costly and limit small companies from integrating them into their development process.We propose a contextual thematic approach that extracts the most relevant theme properties of the feature/requirement to address the aforementioned challenges. Our experiments with two proprietary projects reveal significant enhancement of performance (precision and F1 scores are more than 50% in ideal cases) in linking features to three abstractions of code components, i.e., modules, classes, and methods. Our approach is also capable of linking commits to issues in a promising way. Contextual theme extraction enhances the subjective explainability which has not yet been solved with existing approaches. Moreover, we extract several critical characteristics of the feature documents and code structures that are important to consider in both manual and automated techniques. Finally, we present the FSECAM tool for linking features to code components, which can be immediately deployed within the development process and used without much effort and cost in linking code components and commits.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Ideal Clinic Realisation and Maintenance programme implementation in rural KwaZulu-Natal.

The delivery of quality primary healthcare (PHC) services is vital for enhancing the health status of rural communities, yet persistent barriers exist in resource-constrained rural settings. The study explored perspectives on the barriers to and facilitators of implementing the Ideal Clinic Realisation and Maintenance (ICRM) programme as a quality assurance initiative in a rural KwaZulu-Natal subdistrict. Professional nurses and healthcare managers from seven PHC clinics in a rural subdistrict of KwaZulu-Natal and supervising managers from a district hospital participated in this study. Telephonic semi-structured interviews were conducted using a qualitative case study approach with the purposively selected sample. Data were inductively and thematically analysed. Themes included ICRM programme organisation, barriers and facilitators for implementing the ICRM programme. Barriers in rural PHC settings included overburdened clinics, suboptimal infrastructure, staff burnout, poor communication and non-adherence to clinical guidelines. Despite obstacles, programme implementation was facilitated through stakeholder support and teamwork. Participants emphasised the need for infrastructure upgrades, more human and physical resources, and maintenance of stakeholder support. If challenges are mitigated and supportive factors are leveraged, the potential for successful programme implementation and improved healthcare delivery can benefit both healthcare providers and recipients.Contribution:Through providing insight into the perspectives of both implementers and supervisors, the study informs stakeholders and policymakers about difficulties encountered and potential improvements to be made in the implementation of the ICRM programme in rural PHC.