Relevant bibliographies by topics / Glucose tracking

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Glucose tracking'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Glucose tracking"

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Shah, Ankit M., and Fredric E. Wondisford. "Tracking the carbons supplying gluconeogenesis." Journal of Biological Chemistry 295, no. 42 (2020): 14419–29. http://dx.doi.org/10.1074/jbc.rev120.012758.

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As the burden of type 2 diabetes mellitus (T2DM) grows in the 21st century, the need to understand glucose metabolism heightens. Increased gluconeogenesis is a major contributor to the hyperglycemia seen in T2DM. Isotope tracer experiments in humans and animals over several decades have offered insights into gluconeogenesis under euglycemic and diabetic conditions. This review focuses on the current understanding of carbon flux in gluconeogenesis, including substrate contribution of various gluconeogenic precursors to glucose production. Alterations of gluconeogenic metabolites and fluxes in T
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van den Brink, Willem J., Tim J. van den Broek, Salvator Palmisano, Suzan Wopereis, and Iris M. de Hoogh. "Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies." Nutrients 14, no. 21 (2022): 4465. http://dx.doi.org/10.3390/nu14214465.

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Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity
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Hameed, Rabab Alaa. "The Intelligent Auto-Tuning Controller Design Based on Dolphin Echo Location for Blood Glucose Monitoring System." Journal of Engineering 27, no. 8 (2021): 1–18. http://dx.doi.org/10.31026/j.eng.2021.08.01.

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This paper presents an enhancement technique for tracking and regulating the blood glucose level for diabetic patients using an intelligent auto-tuning Proportional-Integral-Derivative PID controller. The proposed controller aims to generate the best insulin control action responsible for regulating the blood glucose level precisely, accurately, and quickly. The tuning control algorithm used the Dolphin Echolocation Optimization (DEO) algorithm for obtaining the near-optimal PID controller parameters with a proposed time domain specification performance index. The MATLAB simulation results for
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Liu, Weijiu. "A mathematical model for the robust blood glucose tracking." Mathematical Biosciences and Engineering 16, no. 2 (2019): 759–81. http://dx.doi.org/10.3934/mbe.2019036.

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Greyling, Cornelia Felicia, Sasya Madhurantakam, Kai-Chun Lin, Sriram Muthukumar, and Shalini Prasad. "Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat." Biosensors and Bioelectronics: X 14 (September 2023): 100357. http://dx.doi.org/10.1016/j.biosx.2023.100357.

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Kang, Jeon Woong, Yun Sang Park, Hojun Chang, et al. "Direct observation of glucose fingerprint using in vivo Raman spectroscopy." Science Advances 6, no. 4 (2020): eaay5206. http://dx.doi.org/10.1126/sciadv.aay5206.

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Noninvasive blood glucose monitoring has been a long-standing dream in diabetes management. The use of Raman spectroscopy, with its molecular specificity, has been investigated in this regard over the past decade. Previous studies reported on glucose sensing based on indirect evidence such as statistical correlation to the reference glucose concentration. However, these claims fail to demonstrate glucose Raman peaks, which has raised questions regarding the effectiveness of Raman spectroscopy for glucose sensing. Here, we demonstrate the first direct observation of glucose Raman peaks from in
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Timko, Michael T., Alex R. Maag, Juan Mauricio Venegas, et al. "Spectroscopic tracking of mechanochemical reactivity and modification of a hydrothermal char." RSC Advances 6, no. 15 (2016): 12021–31. http://dx.doi.org/10.1039/c5ra24561c.

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Ziegler, Susan E., Paul M. White, Duane C. Wolf, and Gregory J. Thoma. "TRACKING THE FATE AND RECYCLING OF 13C-LABELED GLUCOSE IN SOIL." Soil Science 170, no. 10 (2005): 767–78. http://dx.doi.org/10.1097/00010694-200510000-00002.

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Ashley, Brandon, and Weijiu Liu. "Asymptotic tracking and disturbance rejection of the blood glucose regulation system." Mathematical Biosciences 289 (July 2017): 78–88. http://dx.doi.org/10.1016/j.mbs.2017.05.001.

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Gloes, Franziska, Andrea Boehme, Thilo Liebscher, Steffen Zinn, Maria Richetta, and Andreas H. Foitzik. "Analyte Tracking for Novel Bio-Applications." Materials Science Forum 941 (December 2018): 2454–57. http://dx.doi.org/10.4028/www.scientific.net/msf.941.2454.

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Modern cell culture as well as sophisticated bio-applications involve complex biochemical processes, which are required to induce growth, product development or material degradation. Tracking the reaction processes inside the application presents a major challenge due to its complexity. The development of new analysis and tracking mechanisms for such application presents a solution to fully understand the process. In addition, the applied sensors are required to monitor the reactions enable a live tracking of the process. Furthermore, this gives the opportunity to influence and manipulate reac
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Dissertations / Theses on the topic "Glucose tracking"

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Rivera, Rodriguez Jose A. "Seniors with Diabetes-Investigation of the Impact of Semantic Auditory Distractions on the Usability of a Blood Glucose Tracking Mobile Application." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/64.

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Diabetes is the seventh leading cause of death in the United States. With the population rapidly aging, it is expected that 1 out of 3 Americans will have diabetes by 2050. Mobile devices and mobile applications have the potential to contribute to diabetes self-care by allowing users to manage their diabetes by keeping track of their blood glucose levels. Usability is important for systems that help people self-manage conditions such as diabetes. Age and diabetes-related cognitive decline might intensify the impact of usability issues for the users who need these mobile applications the most.
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Green, Katherine J. "The effect of acute exercise on T-lymphocyte function." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36777/1/36777_Digitised%20Thesis.pdf.

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An increased incidence of illness has been observed in athletic populations undergoing intensive training and competition. T-lymphocytes are central to cell-mediated adaptive immune responses and have been the subject of many studies investigating the relationship between T-lymphocyte function, exercise and athlete health. A decrease in T-lymphocyte function following acute intensive exercise has commonly been described, making them a possible factor contributing to increased susceptibility in athlete populations. However, there is much controversy regarding the interpretation of traditional m
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Leal, Alexandra Carvalhais. "Glucose Tracking." Master's thesis, 2010. http://hdl.handle.net/10348/535.

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Dissertação de Mestrado em Engenharia Electrotécnica e de Computadores<br>A qualidade de vida nos dias de hoje é suportada por capacidades médicas que não estavam disponíveis num passado recente. Estas capacidades não estão limitadas ao tratamento de doenças como a diabetes mellitus, de facto tem havido uma alteração de paradigma para a sua prevenção e monitorização, oferecendo um impacto positivo global no dia-a-dia da gestão destas. A necessidade e o potencial em desenvolver novos e pequenos sistemas de detecção de glicose que permitam contribuir para a adesão dos pacientes aos testes freque
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"Tracking of fasting serum glucose, insulin, and HOMA from 1976--2001: The Bogalusa Heart Study." Tulane University, 2003.

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Background. Little information is available on tracking of glucose or insulin. The phenomenon known as tracking, defined as the maintenance of relative rank with respect to peers over time, is important in the early identification of individuals who may be at highest risk of metabolic disorders. Previous studies indicate that the tracking phenomenon may be most useful for anthropometric variables, slightly less so for lipids, and lowest for blood pressure Methods. Serial, cross-sectional data collected from 1976 to 2001 as part of the Bogalusa Heart Study were combined to create a longitudinal
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Books on the topic "Glucose tracking"

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Designs, Srvb. Glucose Tracking Log. Independently Published, 2020.

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Designs, Srvb. Glucose Tracking Log. Independently Published, 2020.

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Press, Obsidian. Glucose Log: 6-Month Tracking. Independently Published, 2021.

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Blood Sugar Trackers Log Books. Tracking My Blood Glucose Log Book: Your 6"x9" blood glucose monitoring tracking system. Independently published, 2018.

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Journals, Useful. Diabetes Glucose and Food Tracking Journal. Independently Published, 2021.

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Nicole, R. Schmitt. Glucose Log Book: Tracking Blood Sugar. Independently Published, 2021.

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Notebooks, Bell. Glucose Monitoring Log: 53 WEEKS of DAILY BLOOD GLUCOSE TRACKING. Independently Published, 2018.

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hida, mark piwa. Log Book for Tracking Blood Glucose: Simple Weekly Diabetes Blood Glucose Tracking Log Book Before-After. Independently Published, 2021.

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Journals, ColorMePositive. Glucose Tracking Log: Daily Glucose Log Book for Monitoring Your Blood Glucose Levels - Sunflowers. Independently Published, 2018.

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Publishing, Rogue Plus. Glucose Monitoring Log: Blood Glucose Record Sheet, Diabetic Glucose Logbook, Diabetes Blood Glucose Chart, Glucose Tracking Chart, Cute Unicorns Cover. CreateSpace Independent Publishing Platform, 2018.

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Book chapters on the topic "Glucose tracking"

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Villarreal, Paula, Rahul Pal, and Gracie Vargas. "In Vivo Epithelial Metabolic Imaging Using a Topical Fluorescent Glucose Analog." In Cell Tracking. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0364-2_3.

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Arnold Corey W., Nguyen Tina, and Janzen Carla. "BabySTEPS: A Sugar Tracking Electronic Portal System for Gestational Diabetes." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-289-9-1123.

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Gestational diabetes is a condition occurring in up to 18% [1] of pregnant women that results in an increase in blood glucose levels due to the body's inability to produce sufficient insulin given the additional needs of the baby, and/or hormonal changes that lower the body's sensitivity to insulin. If left untreated, the growing baby may become too large, increasing the risk of injury to the mother and baby during delivery. Controlling blood glucose can be a challenging task, especially for women with no previous experience and who may have unhealthy diets. An opportunity exists to further en
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Gyuk Peter, Szabo Istvan, Vassanyi Istvan, Kosa Istvan, and Kovacs Levente. "Combined Model for Diabetes Lifestyle Support." In Studies in Health Technology and Informatics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-389-6-77.

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Treatment of diabetes mellitus is a public health related problem of modern healthcare. Surveys show that current methods to estimate the required amount of insulin are quite inefficient in practice as they are based on experience. This paper offers a new approach to predict the glucose level of people with diabetes. It combines two efficient models of the literature: one for nutrient absorption and one for glucose control. The combination of them tracks the blood sugar level considering nutrition composition, applied insulin and initial glucose level. Compared to already existing mixed meal m
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Kashif, Muhammad, Sergio Flesca, and Pierangelo Veltri. "GLSTM: On Using LSTM for Glucose Level Prediction." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240067.

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The Prediabetes impacts one in every three individuals, with a 10% annual probability of transitioning to type 2 diabetes without lifestyle changes or medical interventions. It’s crucial to manage glycemic health to deter the progression to type 2 diabetes. In the United States, 13% of individuals (18 years of age and older) have diabetes, while 34.5% meet the criteria for prediabetes. Diabetes mellitus and prediabetes are more common in older persons. Currently, nevertheless, there aren’t many noninvasive, commercially accessible methods for tracking glycemic status to help with prediabetes s
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Wahid Anwar, Abdul, Zahid Anwar, Iqra Dildar, Nazakat Ali, Uzba, and Kashif Ahsan. "Biosensing Basics." In Biomedical Engineering. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.113771.

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The aim of this chapter is to understand biosensor basics. A biosensor is a sophisticated analytical device that uses a biological sensing component to detect biological or chemical reactions. It combines an electronic component with a biological element, producing quantifiable signals and detects physiological changes, environmental components, diseases, harmful chemicals, and pH values in various sizes and designs. Biosensors detect substances by detecting an analyte, such as glucose, creatinine, lactate, L-phenylalanine, L-alanine, pyruvate, salicylate, and urea. Biosensors, including DNA,
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Chomutare Taridzo, Tatara Naoe, Årsand Eirik, and Hartvigsen Gunnar. "Designing a diabetes mobile application with social network support." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-266-0-58.

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Although mobile applications and social media have emerged as important facets of the Internet, their role in healthcare is still not well-understood. We present design artefacts, inspired by persuasive technology concepts, from a study of social media as part of a diabetes mHealth application. We used the design science approach for mobile application design, and real-life user testing and focus group meetings to test the application over a 12-week period with 7 participants. Based on the System Usability Score (SUS), the mobile application scored an average of 84.6 (SD=13.2), which represent
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Prakash Sah, Om. "Medical Nutrition Therapy for Type I Diabetes Mellitus." In Type 1 Diabetes Mellitus [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108619.

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Diabetes mellitus is described by high blood glucose level resulting from deficiencies in insulin secretion, insulin action, or both. Type 1 diabetes is a condition in which pancreatic beta-cell get destructed and leads to absolute insulin deficiency. Lack of insulin causes hyperglycemia, polyuria, polydipsia, polyphagia, body mass loss, dehydration, electrolyte disturbance, and ketoacidosis. MNT necessitates an individualized tactic and effective nutrition self-management education, recommendation, and support. A key component of MNT is the provision of adequate calories for normal growth and
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Chehal, Dimple, Parul Gupta, Payal Gulati, Rosy Madaan, and Komal Kumar Bhatia. "Stress Detection Using Machine Learning Techniques in IoT Environment." In Smart Healthcare for Sustainable Urban Development. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2508-4.ch004.

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Employment of various sensors used in IoT can help create a sustainable urban life. Rapid advancements in IoT have made human existence smarter. Smartness may be associated to office, home, networks, energy consumption, agriculture, education, retail, and even healthcare. Smart health management addresses its populations, such as tracking routine activities, obesity, nutrition intake, heart rate, glucose level, oxygen level, body temperature, or even stress level monitoring. Stress is a condition which is being faced by people irrespective of their age, gender, or profession. But its identific
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Sumathi, A., and S. Meganathan. "Diabetes Prediction Model Using Stochastic Gradient Descent Logistic Regression Approach." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1694-8.ch013.

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Diabetes is a chronic disorder caused by either inadequate insulin production by the pancreas or inadequate insulin absorption by the body. Many machine learning approaches handle a wide range of chronic conditions and keep track of patient health data. The analysis of medical data from various angles and the creation of knowledge from it can be accomplished using a variety of machine learning techniques. Creating new features by combining two or more features can provide more insights for health-related data. It aids in revealing a data set's hidden relationships. This work implements LR, RFE
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G., Jayanthi, Purushothaman R., Sivant Moduguru, et al. "Assessment of Cardiac Dynamics and Risk Factor Analysis Using Deep Neural Nets." In Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8443-9.ch007.

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Cardiovascular disease (CVD) is a medical condition that leads to risk of heart disease such as stroke or cardiac arrest. Cardiac attack is a medical condition found in different age groups irrespective of gender. In a clinical study, there are many ways of interpreting the risk factors. The most common risk factors indicating sudden cardiac arrest are glucose, body mass index (BMI), and habitation such as smoking. The difficulties faced by the clinicians are the primary focus of this study. The complexity in clinical stages in examination of medical condition needs to be resolved considering
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Conference papers on the topic "Glucose tracking"

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Shi, Ge, Shihong Zou, and Anpeng Huang. "Glucose-tracking: A postprandial glucose prediction system for diabetic self-management." In 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech). IEEE, 2015. http://dx.doi.org/10.1109/ubi-healthtech.2015.7203318.

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Kovacs, Levente, Peter Szalay, Balazs Benyo, and Geoffrey J. Chase. "Asymptotic output tracking in blood glucose control. A case study." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6161400.

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Tamilselvi, M., and G. Ramkumar. "Non-invasive tracking and monitoring glucose content using near infrared spectroscopy." In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2015. http://dx.doi.org/10.1109/iccic.2015.7435728.

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Kascheev, Nikolay, Oleg Kozyrev, Maxim Leykin, and Alexey Vanyagin. "Non-invasive monitoring of blood glucose by means of wearable tracking technology." In 2017 IEEE East-West Design & Test Symposium (EWDTS). IEEE, 2017. http://dx.doi.org/10.1109/ewdts.2017.8110137.

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Sabzevar, Peyman Vafadoost, Hamid Sadrian, and Ahmad Hajipour. "Tracking Blood Glucose Concentration of Type 1 Diabetic Patients Using Reinforcement Learning." In 2023 9th International Conference on Control, Instrumentation and Automation (ICCIA). IEEE, 2023. http://dx.doi.org/10.1109/iccia61416.2023.10506390.

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Rena Wells, Angela, Leonard KURURAGIREH Bakahika, Reuben Muhindo, et al. "Empowering Diabetes Care: Development and Evaluation of a Smart Medication Reminder and Glucose Tracking App for Patients in Uganda." In InSITE 2025: Informing Science + IT Education Conferences: Hiroshima. Informing Science Institute, 2025. https://doi.org/10.28945/5542.

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Aim/Purpose To provide a solution to lack of proper diabetes management amongst the Ugandan populace who fail to take their medications as and when due and lack access to proper blood sugar status information. Background This paper provides a smart medication reminder and glucose tracking application for diabetes patient in Uganda. Methodology The application incorporates reminders for medication compliance, daily glucose tracking, and tailored health advice, enabling individuals to manage their condition more efficiently. The application mitigates literacy and accessibility difficulties encou
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Yeh, Shu-jen, Stan Kantor, Charles Hanna, Eric Shain, Ronald Hohs, and Omar S. Khalil. "Longitudinal 3-week tracking of blood glucose concentration from thermo-optical response measurements on human skin." In SPIE Proceedings, edited by Valery V. Tuchin. SPIE, 2006. http://dx.doi.org/10.1117/12.697065.

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Perera, W. T. A. N., M. H. Medagedara, and C. N. Herath. "Investigating pomegranate rind dye for wearable health monitoring: a colorimetric approach to analyzing sweat, glucose, and insulin." In Engineering Research Unit Symposium 2023. Engineering Research Unit, 2023. http://dx.doi.org/10.31705/eru.2023.4.

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This study investigates the potential of pomegranate rind dye, a natural source of pH-sensitive anthocyanins, in the field of health monitoring through the colorimetric detection of sweat analytes, markers of physiological states [1], [2]. Addressing the limitations of cost and complexity inherent in traditional sweat analysis methods prevalent in healthcare and sports science [2], [3], our approach utilizes colorimetric analysis. This technique, simpler and more cost-effective, relies on visual color changes in response to specific analytes for quantitative data acquisition [1]. Emphasizing s
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Baranyi, Rene, Rainer Willinger, Nadja Lederer, Florian Walcher, and Thomas Grechenig. "DiaBeaThis — A gamified self-tracking portal to support people suffering from diabetes mellitus to control their blood glucose level." In 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH). IEEE, 2018. http://dx.doi.org/10.1109/segah.2018.8401352.

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Heidmann, Frank, and Boribun Wisanukorn. "Collaborative Learning through XR. A study of eye- and hand-based XR interactions to support collaborative learning in the chemistry classroom." In 12th International Conference on Human Interaction and Emerging Technologies (IHIET 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005488.

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In this paper, we explore how extended reality (XR) and virtual reality (VR) can facilitate shared experiences that transcend individual boundaries. Using an example from the chemistry classroom, we illustrate how these technologies can enhance education and learning by fostering collaboration and shared understanding.Despite the vast potential of XR and VR, many of their applications remain isolated. Currently, sharing experiences often involves streaming onto a screen or verbal explanations, which can leave individuals feeling excluded if they are not directly involved. Even when multiple us
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Reports on the topic "Glucose tracking"

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Chaparadza, Diana. An Analysis of Patient-Generated Health Data in Assisting Nurses and Physicians to Better Treat Patients with Hypertension. University of Tennessee Health Science Center, 2020. http://dx.doi.org/10.21007/chp.hiim.0080.

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Patient Generated Health Data (PGHD is not new but it has gained more attention these past years due to the advent of smart devices, remote monitoring devices and many applications on various smart devices. PGHD reflects medications and treatment, lifestyle choices, and health history. Unlike traditional medical visits, where clinicians collect and manage data within their offices, PGHD is collected by patients throughout the course of their day and provides an insight of how they are responding to treatments or lifestyle choices. Examples include blood glucose monitoring or blood pressure rea
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