IU Indianapolis ScholarWorks :: Browsing by Subject "hypoglycemia"

Browsing by Subject "hypoglycemia"

Now showing 1 - 7 of 7

A Case of Refractory Hypoglycemia with DPP-IV Inhibitors in a Patient with CKD and Paraproteinemia
(Society of Hospital Medicine, 2020-11-05) Lugo, Adrian; Cho, Elizabeth; MacKay, Keir; Thothala, Niranjan
Healthcare Game Design: Behavioral Modeling of Serious Gaming Design for Children with Chronic Diseases
(2009) Kharrazi, Hadi; Faiola, Anthony; Defazio, Joseph
This article introduces the design principles of serious games for chronic patients based on behavioral models. First, key features of the targeted chronic condition (Diabetes) are explained. Then, the role of psychological behavioral models in the management of chronic conditions is covered. After a short review of the existing health focused games, two recent health games that are developed based on behavioral models are overviewed in more detail. Furthermore, design principles and usability issues regarding the creation of these health games are discussed. Finally, the authors conclude that designing healthcare games based on behavioral models can increase the usability of the game in order to improve the effectiveness of the game’s desired healthcare outcomes.
HYPOalert: Designing Mobile Technology for Hypoglycemic Detection and Monitoring--Based on Human Breath
(ACM, 2018-05) Faiola, Anthony; Vatani, Haleh; Greenhill, Kate; Bhuma, Manjula; Agarwal, Mangilal; Mechanical and Energy Engineering, School of Engineering and Technology
Hypoglycemia (HYPO) is characterized by low blood glucose (BG)--leading to complications such as sweating, weakness, passing-out, coma, and even death. Effective HYPO management is required to avoid complications and to increase quality of life. Recently, a noninvasive smart breathing sensor was developed for detection of HYPO in human breath (HYPOalert). The device has the ability to deliver data (via Bluetooth) to a mobile application--with the intent to support Type 1 and 2 diabetics with the self-management of their hypoglycemia. This paper presents the first two (prototype) design iterations of research and testing of HYPOalert. Twelve Type 1 and 2 diabetics were interviewed to deduce user requirements and to understand their perception and level of interest in the proposed mobile system. Outcomes informed a human-centered design process of the interactive prototype, currently under final testing. Results were positive--showing that users were very interested in HYPOalert's use of visualization, as well as its HYPO monitoring and alert system that supports diabetes patients' healthy lifestyle management.
Hypoglycemic Detection by Human Breath: A Mobile Health App that Alerts Diabetics of Low Blood Glucose
(2019-12) Faiola, A.; Vatani, H.; Agarwal, M.; Mechanical and Energy Engineering, School of Engineering and Technology
Low blood glucose (BG) or hypoglycemia (HYPO) can lead to severe health complications such as weakness and unconsciousness. To avoid problems BG self-management is needed. We developed a non-invasive breathing system (HYPOalert) to detect HYPO in human-breath, that sends warning alerts and data visualization to monitor progress. This paper presents two HYPOalert prototype iterations with testing results. Of 14 Type 1/2 diabetics tested, only 10% were pleased with existing monitoring systems and 85% expressed interest in using HYPOalert more than 20x a day. The usability study showed that 92% agreed-strongly agreed with the HYPOalert design, including color/menus/navigation/typography; and 64% felt positive about the apps consistency, flexibility, and info architecture. A post-test survey provided a satisfaction score: 6.64/10, with an open-ended interview showing that HYPOalert could positively impact lifestyle practices, self-managing, and help advance an understanding of the disease.
Predictive Modeling of Hypoglycemia for Clinical Decision Support in Evaluating Outpatients with Diabetes Mellitus
(Taylor & Francis, 2019) Li, Xiaochun; Yu, Shengsheng; Zhang, Zuoyi; Radican, Larry; Cummins, Jonathan; Engel, Samuel S.; Iglay, Kristy; Duke, Jon; Baker, Jarod; Brodovicz, Kimberly G.; Naik, Ramachandra G.; Leventhal, Jeremy; Chatterjee, Arnaub K.; Rajpathak, Swapnil; Weiner, Michael; Biostatistics, School of Public Health
Objective: Hypoglycemia occurs in 20–60% of patients with diabetes mellitus. Identifying at-risk patients can facilitate interventions to lower risk. We sought to develop a hypoglycemia prediction model. Methods: In this retrospective cohort study, urban adults prescribed a diabetes drug between 2004 and 2013 were identified. Demographic and clinical data were extracted from an electronic medical record (EMR). Laboratory tests, diagnostic codes and natural language processing (NLP) identified hypoglycemia. We compared multiple logistic regression, classification and regression trees (CART), and random forest. Models were evaluated on an independent test set or through cross-validation. Results: The 38,780 patients had mean age 57 years; 56% were female, 40% African-American and 39% uninsured. Hypoglycemia occurred in 8128 (539 identified only by NLP). In logistic regression, factors positively associated with hypoglycemia included infection, non-long-acting insulin, dementia and recent hypoglycemia. Negatively associated factors included long-acting insulin plus sulfonylurea, and age 75 or older. The models’ area under curve was similar (logistic regression, 89%; CART, 88%; random forest, 90%, with ten-fold cross-validation). Conclusions: NLP improved identification of hypoglycemia. Non-long-acting insulin was an important risk factor. Decreased risk with age may reflect treatment or diminished awareness of hypoglycemia. More complex models did not improve prediction.
A randomized study on the usefulness of an electronic outpatient hypoglycemia risk calculator for clinicians of patients with diabetes in a safety-net institution
(Taylor & Francis, 2020) Weiner, Michael; Cummins, Jonathan; Raji, Annaswamy; Ofner, Susan; Iglay, Kristy; Teal, Evgenia; Li, Xiaochun; Engel, Samuel S.; Knapp, Kristina; Rajpathak, Swapnil; Baker, Jarod; Chatterjee, Arnaub K.; Radican, Larry; Medicine, School of Medicine
Objective: Hypoglycemia (HG) occurs in up to 60% of patients with diabetes mellitus (DM) each year. We assessed a HG alert tool in an electronic health record system, and determined its effect on clinical practice and outcomes. Methods: The tool applied a statistical model, yielding patient-specific information about HG risk. We randomized outpatient primary-care providers (PCPs) to see or not see the alerts. Patients were assigned to study group according to the first PCP seen during four months. We assessed prescriptions, testing, and HG. Variables were compared by multinomial, logistic, or linear model. ClinicalTrials.gov ID: NCT04177147 (registered on 22 November 2019). Results: Patients (N = 3350) visited 123 intervention PCPs; 3395 patients visited 220 control PCPs. Intervention PCPs were shown 18,645 alerts (mean of 152 per PCP). Patients’ mean age was 55 years, with 61% female, 49% black, and 49% Medicaid recipients. Mean baseline A1c and body mass index were similar between groups. During follow-up, the number of A1c and glucose tests, and number of new, refilled, changed, or discontinued insulin prescriptions, were highest for patients with highest risk. Per 100 patients on average, the intervention group had fewer sulfonylurea refills (6 vs. 8; p < .05) and outpatient encounters (470 vs. 502; p < .05), though the change in encounters was not significant. Frequency of HG events was unchanged. Conclusions: Informing PCPs about risk of HG led to fewer sulfonylurea refills and visits. Longer-term studies are needed to assess potential for long-term benefits.
Towards Development of Smart Nanosensor System To Detect of Hypoglycemia From Breath
(2020-05) Thakur, Sanskar S.; Agarwal, Mangilal; Tovar, Andres; Anwar, Sohel
The link between volatile organic compounds (VOCs) from breath and various diseases and specific conditions has been identified since long by the researchers. Canine studies and breath sample analysis on Gas chromatography/ Mass Spectroscopy has proven that there are VOCs in the breath that can detect and potentially predict hypoglycemia. This project aims at developing a smart nanosensor system to detect hypoglycemia from human breath. The sensor system comprises of 1-Mercapto-(triethylene glycol) methyl ether functionalized goldnanoparticle (EGNPs) sensors coated with polyetherimide (PEI) and poly(vinylidene fluoride -hexafluoropropylene) (PVDF-HFP) and polymer composite sensor made from PVDF-HFP-Carbon Black (PVDF-HFP/CB), an interface circuit that performs signal conditioning and amplification, and a microcontroller with Bluetooth Low Energy (BLE) to control the interface circuit and communicate with an external personal digital assistant. The sensors were fabricated and tested with 5 VOCs in dry air and simulated breath (a mixture of air, small portion of acetone, ethanol at high humidity) to investigate sensitivity and selectivity. The name of the VOCs is not disclosed herein but these VOCs have been identified in-breath and are identified as potential biomarkers for other diseases as well. The sensor hydrophobicity has been studied using contact angle measurement. The GNPs size was verified using Ultra-Violent-Visible (UV-VIS) Spectroscopy. Field Emission Scanning Electron Microscope (FESEM) image is used to show GNPs embedded in the polymer film. The sensors sensitivity increases by more than 400\% in an environment with relative humidity (RH) of 93\% and the sensors show selectivity towards VOCs of interest. The interface circuit was designed on Eagle PCB and was fabricated using a two-layer PCB. The fabricated interface circuit was simulated with variable resistance and was verified with experiments. The system is also tested at different power source voltages and it was found that the system performance is optimum at more than 5 volts. The sensor fabrication, testing methods, and results are presented and discussed along with interface circuit design, fabrication, and characterization.

Browsing by Subject "hypoglycemia"

Results Per Page

Sort Options