Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Proceedings of the National Academy of Sciences of the United States of America
Analytical modeling for suction cup designs for skin-interfaced wearable devices
Stable mounting is a central requirement for skin-interfaced wearable biomedical devices, because accurate and long-term measurements with clinical utility typically demand intimate contact with the skin, whereas practical use also requires gentle removal to minimize skin irritation and damage. Existing mounting strategies often struggle to satisfy these competing requirements simultaneously, especially under prolonged wear or in the presence of sweat and moisture. Suction-based mounting has recently emerged as a promising alternative because it can provide strong, reversible, and adhesive-free attachment, yet its underlying mechanics remain insufficiently understood. Here, we establish analytical models for the deformation and force of suction cups in a fully explicit form, covering both the cone suction cup and an optimized ring suction cup design. Unlike previous approaches that rely on indirect quantities such as the pressure difference and contact radius, which are not available before experiments and therefore cannot serve as controllable design variables, the present framework yields direct relations between suction performance and geometry parameters, material properties, and loading conditions, including the maximum push down displacement and the subsequent pull up displacement. The resulting formulas agree closely with accurate numerical solutions and lead to compact scaling laws that clearly identify how geometry and material parameters govern suction performance. These results provide a quantitative and physically transparent foundation for the design of suction-based mounting strategies in wearable devices.
7 July 2026
Read appraisal →JMIR medical informatics
Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study
BACKGROUND: Smart bioelectronics are electronic medical devices that combine hardware and artificial intelligence (AI)-based software. These convergent medical devices analyze bio-signals measured through hardware using AI algorithms and deliver physical stimulation to enhance therapeutic effects. OBJECTIVE: This study aimed to systematically analyze recent research trends in smart bioelectronics to understand their evolving role in digital health care and to provide evidence-based insights for shaping future research and development strategies. METHODS: A total of 92 publications indexed in PubMed between 2020 and 2024 were analyzed. Latent Dirichlet allocation-based topic modeling, optimized using coherence scores, was applied to identify latent research themes. RESULTS: The results indicate a steady increase in related research over the past 5 years, along with a clear shift in research focus from bio-signal sensing and bioelectronic device materials toward AI-driven analysis and disease-oriented applications, ultimately evolving into intelligent and adaptive bioelectronic therapeutic systems. Three major research topics were identified: bio-signal-based neuromodulation (n=23, 25%), AI-driven neurological disease analysis (n=32, 34.7%), and implantable bioelectronics and biomaterials (n=37, 40.2%). CONCLUSIONS: By mapping the evolving landscape of smart bioelectronics, this study provides valuable insights into their multidisciplinary development and highlights their potential applications in clinical decision support, personalized rehabilitation, and next-generation medical device innovation.
24 June 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Reproducible workflow for online artificial intelligence in digital health
Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data are collected on individuals. Deploying online AI presents a key challenge: balancing adaptability of online AI with reproducibility. Online AI in digital interventions is a rapidly evolving area, driven by advances in algorithms, sensors, software and devices. Digital health intervention development and deployment is a continuous process, where implementation-including the AI decision-making algorithm-is interspersed with cycles of re-development and optimization. Each deployment informs the next, making iterative deployment a defining characteristic of this field. This iterative nature underscores the importance of reproducibility: data collected across deployments must be accurately stored to have scientific utility, algorithm behaviour must be auditable and results must be comparable over time to facilitate scientific discovery and trustworthy refinement. This article proposes a reproducible scientific workflow for developing, deploying and analysing online AI decision-making algorithms in digital health interventions. Grounded in practical experience from multiple real-world deployments, this workflow addresses key challenges to reproducibility across all phases of the online AI algorithm development life cycle. This article is part of the theme issue 'Statistical workflow'.
29 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
A systematic review of blood pressure monitor validation studies
BACKGROUND: Good hypertension care requires accurate blood pressure (BP) measurement with BP monitors validated as accurate. The British and Irish Hypertension Society maintains a peer-reviewed list of validated monitors, independent of commercial interests. The list informs National Institute for Health and Care Excellence hypertension guidance and NHS monitor procurement. An updated universal standard protocol for BP device validation was introduced in 2018. AIM: Review the literature to establish which validation protocols are now being used; has this changed since publication of the universal protocol, and what proportion of recently published BP monitor validations fully meet protocol validation criteria? METHOD: Systematic review searches of Medline and Embase from 2018 to September 2025 identified BP monitor validation studies. Citations and full texts were screened independently by two authors. Included studies were reviewed independently by two authors against checklists of protocol criteria. Disagreements were resolved through discussion and/or arbitration by lead authors. RESULTS: Seven reviewers screened 1780 unique citations, reviewing 216 full texts to date. Nine reviewers have double-extracted data from 157 publications reporting 197 device validation studies. Overall, 104 (63%) used universal protocols. While 184 (93%) validations were reported as successful by authors, only 146 (74%) fulfilled all protocol criteria on review. Thus, we considered 51 (26%) published successful validations inadequate, due to non-fulfilment of key validation criteria and/or violations of BP measurement protocols such as incomplete BP range coverage. CONCLUSION: This review highlights inadequacies in current journal peer-review processes, justifying our approach to provide impartial guidance for health professionals, purchasers, and the public.
16 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Levels of analysis for large language models
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to the historical difficulties in understanding the human mind, we argue that methods developed in cognitive science can be useful for understanding large language models. We propose a framework for applying these methods based on the levels of analysis that David Marr proposed for studying information processing systems. By revisiting established cognitive science techniques relevant to each level and illustrating their potential to yield insights into the behaviour and internal organization of large language models, we aim to provide a toolkit for making sense of these new kinds of minds. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
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