NIH Fifth Artificial Pancreas Workshop 2023: Meeting Report: The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption

Aaron, Rachel E.; Tian, Tiffany; Yeung, Andrea M.; Huang, Jingtong; Arreaza-Rubín, Guillermo A.; Ginsberg, Barry H.; Kompala, Tejaswi; Lee, Wei-An (Andy); Kerr, David; Colmegna, Patricio; Mendez, Carlos E.; Muchmore, Douglas B.; Wallia, Amisha; Klonoff, David C.

doi:10.1177/19322968231201829

Cited by 2 publications

(1 citation statement)

References 65 publications

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“…Scholars are currently more focused on pushing the boundaries of innovation with wearable sensing tattoos, neglecting essential considerations such as medical device standards, regulatory frameworks, and cybersecurity [69][70][71]. In contrast, established implantable devices like pacemakers and cutting-edge innovations like artificial pancreases receive dedicated attention on these critical themes [72][73][74]. This discrepancy underscores a gap in the current trajectory of research and development within the domain of sensing tattoos.…”

Section: Limitations and Suggestions For A Broader Investigationmentioning

confidence: 99%

Smart Tattoo Sensors 2.0: A Ten-Year Progress Report through a Narrative Review

Pirrera,

Giansanti

2024

Bioengineering

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The increased interest in sensing tattoos reflects a shift in wearable technology, emphasizing their flexible, skin-adherent nature. These devices, driven by advancements in nanotechnology and materials science, offer highly sensitive and customizable sensors. The growing body of research in this area indicates a rising curiosity in their design and applications, with potential uses ranging from vital sign monitoring to biomarker detection. Sensing tattoos present a promising avenue in wearable healthcare technology, attracting attention from researchers, clinicians, and technology enthusiasts. The objective of this study is to analyze the development, application, and integration of the sensing tattoos in the health domain. A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 37 studies. Sensing tattoos hold transformative potential in health monitoring and physiological sensing, driven by their focus on affordability, user-friendly design, and versatile sensorization solutions. Despite their promise, ongoing refinement is essential, addressing limitations in adhesion, signal quality, biocompatibility, and regulatory complexities. Identified opportunities, including non-invasive health monitoring, multiplexed detection, and cost-effective fabrication methods, open avenues for personalized healthcare applications. However, bridging gaps in medical device standards, cybersecurity, and regulatory compliance is imperative for seamless integration. A key theme calls for a holistic, user-centric approach, emphasizing interdisciplinary collaboration. Balancing innovation with practicality, prioritizing ethics, and fostering collaboration are crucial for the evolution of these technologies. The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the responsible integration of sensing tattoos into daily life.

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Section: Limitations and Suggestions For A Broader Investigationmentioning

confidence: 99%

Smart Tattoo Sensors 2.0: A Ten-Year Progress Report through a Narrative Review

Pirrera,

Giansanti

2024

Bioengineering

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Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

Pai,

Barua,

Kim

et al. 2024

J Diabetes Sci Technol

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Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices’ impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment. Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D). Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants’ CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D. Results: Our algorithm’s estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise ( P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set ( P = .005) but not in the validation T2D data set ( P = .18). Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

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NIH Fifth Artificial Pancreas Workshop 2023: Meeting Report: The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption

Cited by 2 publications

References 65 publications

Smart Tattoo Sensors 2.0: A Ten-Year Progress Report through a Narrative Review

Smart Tattoo Sensors 2.0: A Ten-Year Progress Report through a Narrative Review

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

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