Health Technology Research Articles

Healthcare Management Approaches for Improving Employee Occupational Safety

Introduction: in healthcare environments, occupational safety is rather crucial to guarantee that patients and staff get the best treatment available. Healthcare professionals run many hazards, including injury, disease, and extreme emotional stress; all of which may significantly affect their performance and general state of health. Thus, one crucial component of healthcare administration is ensuring employees’ safety at work.Method: this paper investigates many approaches to provide occupational safety for medical professionals. Every present safety regulation, training initiative, and technological solution was thoroughly examined. Data was obtained from many medical institutions using these approaches. Among the tools used were surveys, talks with staff members and healthcare management, and analysis of safety incident data.Results: safety management systems, consistent safety training, the use of personal protective equipment (PPE), and mental health support programs have helped to reduce the incidence of accidents, infections, and stress-related issues occurring at work, the research showed. Additionally demonstrated to reduce physical stress is the use of technology and robotics, therefore enhancing the safety and efficiency of many fields.Conclusion: the findings reveal that a multifarious approach is required to make healthcare professionals safer. This includes utilising mental health assistance, training, and technology. To safeguard the health and safety of their staff, healthcare providers should rank these actions first among their objectives. This will raise patient receiving quality of treatment. Future studies should investigate the long-term consequences of these developments as well as other technological innovations potentially used in safety management.

Usability Evaluation Satu Sehat Application Using Heuristic Evaluation Method

Digital transformation in the health sector aims to improve efficient, effective, and integrated services. The government has started building mobile-based applications in various fields. Satu Sehat application is one of mobile-based application developed by the Indonesian Ministry of Health. As an integrated health platform in Indonesia, faces challenges in ensuring optimal user experience. User experience is greatly influenced by a good user interface. User interface must have good usability aspect so that users will feel comfortable when using the application. This study aims to evaluate the usability of the application using the Heuristic Evaluation method, by identifying problems based on Nielsen's 10 heuristic principles. Evaluation was conducted by 5 evaluators who have expertise in interaction design, health technology, and usability analysis. The results reveal two problems in error prevention and one problem in help and documentation, which are the basis for recommendations for further development. Recommendations are given on several aspects to improve the user experience.

Implementation and evaluation of online and offline blended teaching model in laboratory course of Clinical Laboratory Hematology

This study explores the implementation and evaluation of online and offline blended teaching model in the Clinical Laboratory Hematology (CLH) laboratory course at Chengdu Medical College. To this end, 90 students majoring in medical laboratory technology in 2020 were selected as research subjects and randomly divided into experimental and control groups. The experimental group adopted an online and offline blended teaching model, while the control group adopted a traditional one. After-class tests, laboratory examinations, questionnaire surveys, and student interviews were conducted to evaluate the teaching effectiveness. The average scores of both tests and examinations in the experimental group were significantly higher than those in the control group (p < 0.05). Furthermore, the results of the feedback questionnaire showed that the satisfaction and self-evaluation of the learning effect of students in the experimental group were significantly better than those in the control group (p < 0.05). The blended teaching model has been successful and popular in CLH. This is a useful teaching mode worth applying to laboratory teaching in medical courses.

Dr. Krishna Manohar Soman Rema: A Legacy of Innovation, Education, and Compassion in Pediatric Cardiac Surgery.

Dr. Krishna Manohar Soman Rema (SR) (1956-2022) was a pioneering congenital cardiac surgeon and an inspiring educator who dedicated his life to advancing pediatric cardiac care and mentoring the next generation of surgeons. Trained in cardiac surgery at the Sree Chitra Tirunal Institute for Medical Sciences and Technology in Kerala, he served as faculty there for 20 years, contributing significantly to the development of the Chitra Heart Valve. His career also included tenures at the Madras Medical Mission, Frontier Lifeline Hospital, and the Sri Sathya Sai Hospital in Whitefield, Bengaluru. Dr. Krishna Manohar's most enduring contribution was his role in establishing cardiac surgical services at the Sri Sathya Sai Sanjeevani International Centre for Child Heart Care and Research in Palwal, Haryana, part of a network of hospitals offering free congenital heart surgery to underprivileged children. Under his leadership, these centers performed over 22,000 open-heart and interventional procedures over a decade, achieving excellent outcomes. An exceptional mentor, Dr. Krishna Manohar introduced a transformative ABCDframework for cardiac surgery, emphasizing adaptability, befriending collaboration, compassion, and discipline. He employed innovative teaching methods, such as the Handicraft Your Own Hearttechnique, to simplify complex concepts for trainees. Recognized for his dedication, he received numerous accolades, including the Lifetime Achievement Award from the Pediatric Cardiac Society of India in 2023. Dr. Krishna Manohar's legacy lives on through his students and the institutions he helped build, inspiring future generations to continue his mission of providing high-quality, accessible care to children with congenital heart disease (CHD).

The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.

An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.

Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings.

In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. In low-resource settings, AI offers significant potential to address health care disparities exacerbated by shortages of medical professionals and other resources. However, implementing AI effectively and responsibly in these settings requires careful consideration of context-specific needs and barriers to equitable care. This article explores the practical deployment of AI in low-resource environments through a review of existing literature and interviews with experts, ranging from health care providers and administrators to AI tool developers and government consultants. The authors highlight 4 critical areas for effective AI deployment: infrastructure requirements, deployment and data management, education and training, and responsible AI practices. By addressing these aspects, the proposed framework aims to guide sustainable AI integration, minimizing risk, and enhancing health care access in underserved regions.

Role of Scavenger Receptor B1 (SR-B1) in Improving Food Benefits for Human Health

Scavenger receptor class B member 1 (SR-B1) is a multiligand receptor with a broad range of functions spanning from the uptake of cholesteryl esters from high-density lipoproteins (HDLs) and transport of micronutrients such as fat-soluble vitamins and carotenoids across cell membranes to roles in tumor progression, pathogen recognition, and inflammatory responses. As a target of exposome factors such as environmental stressors and unhealthy lifestyle choices, as well as aging, dysregulated expression and activity of SR-B1 can negatively impact human health. Intriguingly, not only is SR-B1 a major determinant of nutrient homeostasis and, hence, metabolic health status, but these same nutrients and some phytochemicals have also demonstrated their ability to modulate SR-B1. Therefore, an integrated approach that, taking into account human health, nutrition, and food technology sciences, aims to produce foods with health-promoting effects should take advantage of the multifaceted properties of SR-B1. Improved functional foods and novel nanoparticle-based delivery systems, rich in nutrients and phytochemicals, with precise targeting to SR-B1 in specific tissues or structures could represent a strategic advance to improve human health and promote well-being.

Moral sensitivity and attitudes towards patient safety among critical care nurses.

Patient safety is essential for healthcare quality and a global concern. The rapid advancement of medical technology presents ethical challenges for critical care nurses, who navigate complex decision-making processes. Given their close relationships with patients, nurses are uniquely positioned to address patient safety issues. Thus, enhancing nurses' moral sensitivity and ethical values is increasingly important. This study aims to explore the relationship between moral sensitivity and attitude towards patient safety in critical care nurses. This cross-sectional, descriptive-correlational study was conducted from June to September 2022 in three hospitals affiliated with Hamadan University of Medical Sciences in Iran. Participants were nurses in intensive care units (ICUs), selected via convenience sampling. A total of 382 nurses completed questionnaires assessing demographic information, moral sensitivity, and patient safety attitudes. Data analysis was performed using descriptive statistics, t-tests, ANOVA, Pearson's correlation, and multiple regression in SPSS 26.Ethical considerations: This research received ethical approval from the Ethics Committee of Hamadan University of Medical Sciences. Participants provided informed consent regarding the study's objectives, and their confidentiality was strictly upheld throughout the research. The nurses' average age and work experience were 38.6years and 12.4years, respectively. Mean scores for moral sensitivity and patient safety attitude were 81.65 ± 2.69 and 51.47 ± 2.21, indicating high levels and a significant positive correlation. Factors such as increasing age, work experience, fewer working hours, and completion of ethics training were linked to more positive attitudes towards patient safety (p < .05). Regression analysis showed that 42.8% of the variability in attitudes towards patient safety could be explained by moral sensitivity and demographic characteristics. This study highlights the relationship between moral sensitivity and patient safety attitudes among critical care nurses, emphasizing the importance of education, experience, and training in shaping ethical values. Enhancing moral sensitivity is vital for reducing risky behaviors and improving patient safety outcomes in critical care settings.

Interpretation of change in novel digital measures: a statistical review and tutorial

Background Novel clinical measures assessed by a digital health technology tool require thresholds to interpret change over time, such as the minimal clinically important difference (MCID). Establishing such thresholds is a key component of clinical validation, facilitating understanding of relevant treatment effects. Summary Many of the approaches to derive interpretative thresholds for patient-reported outcomes can be applied to digital clinical measures. We present theoretical background to the use of interpretative thresholds, including the distinction between thresholds based on perceived importance versus measurement error, and thresholds for group- versus individual-level interpretations. We then review methods to estimate such thresholds, including anchor-based approaches. We illustrate the methods using data on cough frequency counts as measured by a wearable device in a clinical trial. Key Messages This paper provides an overview of statistical methodologies to estimate thresholds for the interpretation of change.

Photonic Nanomaterials for Wearable Health Solutions.

This review underscores the transformative potential of photonic nanomaterials in wearable health technologies, driven by increasing demands for personalized health monitoring. Their unique optical and physical properties enable rapid, precise, and sensitive real-time monitoring, outperforming conventional electrical-based sensors. Integrated into ultra-thin, flexible, and stretchable formats, these materials enhance compatibility with the human body, enabling prolonged wear, improved efficiency, and reduced power consumption. A comprehensive exploration is provided of the integration of photonic nanomaterials into wearable devices, addressing material selection, light-matter interaction principles, and device assembly strategies. The review highlights critical elements such as device form factors, sensing modalities, and power and data communication, with representative examples in skin patches and contact lenses. These devices enable precise monitoring and management of biomarkers of diseases or biological responses. Furthermore, advancements in materials and integration approaches have paved the way for continuum of care systems combining multifunctional sensors with therapeutic drug delivery mechanisms. To overcome existing barriers, this review outlines strategies of material design, device engineering, system integration, and machine learning to inspire innovation and accelerate the adoption of photonic nanomaterials for next-generation of wearable health, showcasing their versatility and transformative potential for digital health applications.

Qualitative In-trial Interviews: Methods, Challenges, and Best Practice.

Qualitative in-trial interviews with clinical trial participants are a means of providing rich, in-depth patient experience data to supplement and complement data captured by clinical outcome assessments and other clinical trial efficacy endpoints. Such in-trial interview data can be used to build understanding of disease and treatment experiences, evaluate content validity of clinical outcome assessments, aid interpretation of scores and meaningful changes, inform trial design feasibility and operational considerations, and provide supportive evidence regarding safety, efficacy, and effectiveness. Despite the rapid growth of in-trial interviews as part of clinical development programs in the pharmaceutical industry in recent years, published guidelines regarding the methods, conduct, and implementation of in-trial interviews are scarce. Drawing on published examples and the authors' experiences of conducting in-trial interview studies, this article provides an overview of best practice methods for implementing this methodology (including considerations for study design, sample size, interview conduct, and analysis) and the value of in-trial interview data to answer specific research questions. Operational and logistical considerations are outlined, including recommendations for country selection, site selection, training and communication, adverse event safety reporting, and data management and handling. Well-designed and carefully implemented in-trial interviews can lead to generation of insightful patient experience data that are truly of value to inform regulators, health technology agencies, clinicians, patients, and caregivers about product attributes and the impact of diseases and treatments on patients' lives.

A Soft Robotic Textile‐Actuated Anthropomorphic Artificial Shoulder Mechanism

Replicating the human shoulder in anthropomorphic systems is notoriously challenging due to its complex combination of mobility and strength. This study presents the design, fabrication, and control of a new soft artificial shoulder that achieves a broad range of motion, torque, and compliance. Powered by soft robotic textiles consisting of a network of hydraulic artificial muscles, the engineered shoulder effectively mimics intricate shoulder movements, including flexion/extension, abduction/adduction, and medial/lateral rotation. Experiments demonstrate that the artificial shoulder can generate a peak torque of 9.6 ± 0.1 Nm, covering 65.3% of the human shoulder workspace. The artificial shoulder capability is demonstrated through several experimental testbeds. First, it is employed to develop a gesture‐controlled telemanipulation robotic system, applicable to robot‐assisted surgery, hazardous environment operations, gaming, and rehabilitation. Second, it serves as a platform for simulating and studying neurological disorders, such as Parkinson's disease. This approach offers a reliable in vitro testing ground for wearable device validation, providing a crucial intermediary step before progressing to user studies. The artificial shoulder marks a significant advancement in next‐generation anthropomorphic systems, closely mimicking the human musculoskeletal system, with promising applications in wearable assistive devices, haptics, orthopedic testing, and medical technologies.

Advancements in iron-based photocatalytic degradation for antibiotics and dyes.

The accelerated growth of the economy and advancements in medical technology have led to the discharge of a diverse range of organic pollutants into water sources. Recent investigations into water treatment have demonstrated the potential for integrating photocatalysis with techniques such as photocatalytic persulfate activation and the Photo-Fenton process for more efficient wastewater management. Iron-based photocatalysts responsive to visible light offer several advantages, including non-toxicity, safety, affordability, and excellent chemical and optical properties. Currently, there is a notable increase in research activity focused on the iron-based photocatalytic degradation of antibiotics and dyes. Given their abundance, cost-effectiveness, and eco-friendliness, iron-based photocatalysis shows considerable promise for various applications, including water treatment, air purification, and energy conversion. The use of iron-based photocatalysts has been demonstrated to facilitate the production of more reactive oxygen radicals, achievable through the Photo-Fenton process, direct photocatalysis, and the photocatalytic activation of persulfates. This approach has been demonstrated to enhance the degradation efficiency of antibiotics and dyes. Ongoing research encompasses the preparation and refinement of iron-based materials, exploration of photocatalytic mechanisms, and expansion of practical applications. Future directions include material innovation, elucidation of mechanisms, scaling up applications, and multifunctionalization, with the objective of enhancing photocatalytic efficiency, transitioning the technology from laboratory settings to practical scales, and providing effective solutions to environmental challenges and energy constraints.

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation.

Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges. Method: This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets. Result: Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4%. Conclusion: The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

3D extrusion bioprinting of microbial inks for biomedical applications.

In recent years, the field of 3D bioprinting has witnessed the intriguing development of a new type of bioink known as microbial inks. Bioinks, typically associated with mammalian cells, have been reimagined to involve microbes, enabling many new applications beyond tissue engineering and regenerative medicine. This review presents the latest advancements in microbial inks, including their definition, types, composition, salient characteristics, and biomedical applications. Herein, microbes are genetically engineered to produce 1) extrudable bioink and 2) life-like functionalities such as self-regeneration, self-healing, self-regulation, biosynthesis, biosensing, biosignaling, biosequestration, etc. We also discuss some of the promising applications of 3D extrusion printed microbial inks, such as 1) drugs and probiotics delivery, 2) metabolite production, 3) tissue engineering, 4) bioremediation, 5) biosensors and bioelectronics, 6) biominerals and biocomposites, and 7) infectious disease modeling. Finally, we describe some of the current challenges of microbial inks that needs to be addressed in the coming years, to make a greater impact in health science and technology and many other fields.

Personalized interventions for behaviour change: A scoping review of just-in-time adaptive interventions.

Examine the development, implementation and evaluation of just-in-time adaptive interventions (JITAIs) in behaviour change and evaluate the quality of intervention reporting. A scoping review of JITAIs incorporating mobile health (mHealth) technologies to improve health-related behaviours in adults. We searched MEDLINE, Embase and PsycINFO using terms related to JITAIs, mHealth, behaviour change and intervention methodology. Narrative analysis assessed theoretical foundations, real-time data capturing and processing methods, outcome evaluation and summarized JITAI efficacy. Quality of intervention reporting was assessed using the template for intervention description and replication (TIDieR) checklist. Sixty-two JITAIs across physical activity, sedentary behaviour, dietary behaviour, substance use, sexual behaviour, fluid intake, treatment adherence, social skills, gambling behaviour and self-management skills were included. The majority (71%) aimed to evaluate feasibility, acceptability and/or usability. Supporting evidence for JITAI development was identified in 46 studies, with 67% applying this to develop tailored intervention content. Over half (55%) relied solely on self-reported data for tailoring, and 13 studies used only passive monitoring data. While data processing methods were commonly reported, 44% did not specify their techniques. 89% of JITAI designs achieved full marks on the TIDieR checklist and provided sufficient details on JITAI components. Overall, JITAIs proved to be feasible, acceptable and user-friendly across behaviours and settings. Randomized trials showed tailored interventions were efficacious, though outcomes varied by behaviour. JITAIs offer a promising approach to developing personalized interventions, with their potential effects continuously growing. The recommended checklist emphasizes the importance of reporting transparency in establishing robust intervention designs.

States, global power and access to medicines: a comparative case study of China, India and the United States, 2000–2019

BackgroundWhat constitutes state`s global power to shape access to medicines? How was it distributed between states and how did this change from 2000 to 2019? In this comparative case study, we explored the powers of China, India and the United States, and discuss whether our findings from the pre-pandemic era were reflected in the global COVID-19 response related to pharmaceuticals. We used an analytical framework from the international relations literature on structural power, and assessed the following power structures after adapting them to the context of access to medicines: finance, production, financial protection, knowledge, trade and official development assistance.ResultsWe found that from 2000 to 2019 there had been a power-shift towards China and India in terms of finance and production of pharmaceuticals, and that in particular China had increased its powers regarding knowledge and financial protection and reimbursement. The United States remained powerful in terms of finance and knowledge. The data on trade and official development assistance indicate an increasingly powerful China also within these structures. During the COVID-19 pandemic, we found that the patterns from previous decades were continued in terms of cutting-edge innovation coming out of the United States. Trade restrictions from the United States and India contrasted our findings as well as the limited effective aid from the United States.Building on our findings on structural powers, we argue that both structural power and political decisions shaped access to medical technologies during the COVID-19 pandemic. We also examined the roles and positions of the three states regarding developments in global health governance on the COVAX mechanism, the TRIPS Agreement waiver and the pandemic accord in this context.ConclusionFrom 2000-2019, China and India increased their structural powers to shape global access to medical technologies. The recent COVID-19 pandemic demonstrated that both structural power and political decisions shaped global access to COVID-19 technologies.

Comparison of health technology assessments for digital therapeutics in Germany, the United Kingdom and France.

Digital therapeutics (DTx) are promising technologies. However, current assessment and access frameworks, when they exist, are heterogeneous and fragmented. We analysed and compared health technology assessment (HTA) criteria for DTx across European countries that had assessed the same DTx products. This allowed us to conduct a direct comparison of these countries' DTx assessment frameworks. A scoping review identified HTA DTx documents from European HTA bodies with specific DTx frameworks in place. The HTAs of the same DTx products assessed across multiple countries were then extracted and analysed. Deprexis and Velibra were the only DTx products assessed by multiple countries: Deprexis, indicated for depression, was assessed by Germany, the United Kingdom (UK) and France; and Velibra, indicated for anxiety, was assessed by Germany and the UK. There are notable differences among those frameworks, but what they share is an emphasis on the context-specific positioning of products within the disease landscape, choice of comparators and usage and usability data. Safety data are not a major focus in DTx assessments in these countries, but a request is often made for the addition and evaluation of risk-flagging systems. The majority of European countries do not have a specific framework in place for the assessment of DTx, and major differences exist among recently implemented frameworks. The current policy landscape at the European Union level offers an opportunity to establish a harmonized framework for the assessment of DTx and possibly faster access and integration of such promising technologies.

Annual versus less frequent mammographic surveillance in people with breast cancer aged 50 years and older in the UK (Mammo-50): a multicentre, randomised, phase 3, non-inferiority trial.

The frequency of mammographic surveillance for women after diagnosis of breast cancer varies globally. The aim of this study was to evaluate whether less than annual mammography was non-inferior in terms of breast cancer-specific survival in women aged 50 years or older. Mammo-50 was a multicentre, randomised, phase 3 trial of annual versus less frequent mammography (2-yearly after conservation surgery; 3-yearly after a mastectomy) for women aged 50 years or older at initial diagnosis of invasive or non-invasive breast cancer and who were recurrence free 3 years post curative surgery. The trial was conducted at 114 National Health Service hospitals in the UK. Participants were randomly assigned (1:1) to annual or less frequent mammograms at 3 years post curative surgery and were followed up for 6 years. The co-primary outcomes were breast cancer-specific survival and cost-effectiveness. The cost-effectiveness analysis will be reported elsewhere. Breast cancer-specific survival was assessed in the intention-to-treat population. Secondary outcomes were recurrence-free interval, overall survival, and referrals back to the hospital system. 5000 women provided 90% power to detect a 3% absolute non-inferiority margin for breast cancer-specific survival with 2·5% one-sided significance. The trial was registered with the ISRCTN registry, ISRCTN48534559; recruitment is complete but longer-term follow-up is ongoing. Between April 22, 2014, and Sept 28, 2018, 5235 women were randomly assigned to annual mammography (n=2618) or less frequent mammography (n=2617). 3858 (73·6%) women were aged 60 years or older, 4202 (80·3%) had undergone conservation surgery, 4576 (87·4%) had invasive disease, 1159 (22·1%) had node positive disease, and 4330 (82·7%) had oestrogen receptor-positive tumours. With a median of 5·7 years follow-up (IQR 5·0-6·0; 8·7 years post curative surgery), 343 women died, including 116 who died of breast cancer (61 in the annual mammography group and 55 in the less frequent mammography group). 5-year breast cancer-specific survival was 98·1% (95% CI 97·5-98·6) in the annual mammography group and 98·3% (97·8-98·8) in the less frequent mammography group (hazard ratio 0·92, 95% CI 0·64-1·32), demonstrating non-inferiority of less frequent mammography at the pre-specified 3% margin (non-inferiority p<0·0001). 5-year recurrence-free interval was 94·1% (95% CI 93·1-94·9) in the annual mammography group and 94·5% (93·5-95·3) in the less frequent mammography group. Overall survival at 5 years was 94·7% (95% CI 93·8-95·5%) and 94·5% (93·5-95·3), respectively. 224 (64·9%) of 345 breast cancer events were detected from emergency admissions or symptomatic referrals back to the hospital system, including 108 (61·7%) of 175 in the annual mammography group and 116 (68·2%) of 170 in the less frequent mammography group. For patients aged 50 years or older and at 3 years post diagnosis, less frequent mammograms were non-inferior compared with annual mammograms for breast cancer-specific survival, recurrence-free interval, and overall survival, and should be considered for this population. National Institute for Health Research Health Technology Assessment programme.