Trial reportJournal of diabetes science and technology2016
Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas.
Trial report in Journal of diabetes science and technology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events.Journal of diabetes science and technology · 2023Article
- Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.JMIR diabetes · 2022Review
- Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example.Journal of diabetes science and technology · 2022Article
- A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study.Journal of diabetes science and technology · 2020Article
- Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2020Article
- Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives.Journal of diabetes science and technology · 2019Review
- Multivariable Artificial Pancreas for Various Exercise Types and Intensities.Diabetes technology & therapeutics · 2018Article
- Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.Journal of diabetes science and technology · 2018Article
- Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.Diabetes technology & therapeutics · 2018Article
- Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.Control engineering practice · 2018Article
- Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes.Current diabetes reports · 2017Review
- HYPO-CHEAT's aggregated weekly visualisations of risk reduce real world hypoglycaemia.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Fear of hypoglycemia is a major concern for many patients with type 1 diabetes and affects patient decisions for use of an artificial pancreas system. We propose an alternative way for prevention of hypoglycemia by issuing predictive hypoglycemia alarms and encouraging patients to consume carbohydrates in a timely manner. The algorithm has been tested on 6 subjects (3 males and 3 females, age 24.2 ± 4.5 years, weight 79.2 ± 16.2 kg, height 172.7 ± 9.4 cm, HbA1C 7.3 ± 0.48%, duration of diabetes 209.2 ± 87.9 months) over 3-day closed-loop clinical experiments as part of a multivariable artificial pancreas control system. Over 6 three-day clinical experiments, there were only 5 real hypoglycemia episodes, of which only 1 hypoglycemia episode occurred due to being missed by the proposed algorithm. The average hypoglycemia alarms per day and per subject was 3. Average glucose value when the first alarms were triggered was recorded to be 117 ± 30.6 mg/dl. Average carbohydrate consumption per alarm was 14 ± 7.8 grams. Our results have shown that most low glucose concentrations can be predicted in advance and the glucose levels can be raised back to the desired levels by consuming an appropriate amount of carbohydrate. The proposed algorithm is able to prevent most hypoglycemic events by suggesting appropriate levels of carbohydrate consumption before the actual occurrence of hypoglycemia.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.