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1.
Diabetes Technol Ther ; 23(3): 175-186, 2021 03.
Article in English | MEDLINE | ID: mdl-33048581

ABSTRACT

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755.


Subject(s)
Diabetes Mellitus, Type 1 , Quality of Life , Adult , Artificial Intelligence , Blood Glucose , Cross-Over Studies , Diabetes Mellitus, Type 1/drug therapy , Feasibility Studies , Glycated Hemoglobin/analysis , Humans , Hypoglycemic Agents/therapeutic use , Insulin/therapeutic use , Insulin Infusion Systems , Middle Aged
2.
Sensors (Basel) ; 19(9)2019 Apr 30.
Article in English | MEDLINE | ID: mdl-31052198

ABSTRACT

In this paper, we present a new complex electronic system for facilitating communication with severely disabled patients and telemonitoring their physiological parameters. The proposed assistive system includes three subsystems (Patient, Server, and Caretaker) connected to each other via the Internet. The two-way communication function is based on keywords technology using a WEB application implemented at the server level, and the application is accessed remotely from the patient's laptop/tablet PC. The patient's needs can be detected by using different switch-type sensors that are adapted to the patient's physical condition or by using eye-tracking interfaces. The telemonitoring function is based on a wearable wireless sensor network, organized around the Internet of Things concept, and the sensors acquire different physiological parameters of the patients according to their needs. The mobile Caretaker device is represented by a Smartphone, which uses an Android application for communicating with patients and performing real-time monitoring of their physiological parameters. The prototype of the proposed assistive system was tested in "Dr. C.I. Parhon" Clinical Hospital of Iasi, Romania, on hospitalized patients from the Clinic of Geriatrics and Gerontology. The system contributes to an increase in the level of care and treatment for disabled patients, and this ultimately lowers costs in the healthcare system.


Subject(s)
Disabled Persons , Eye Movement Measurements , Monitoring, Physiologic , Wireless Technology , Aged , Aged, 80 and over , Female , Humans , Image Processing, Computer-Assisted , Male , Quality of Life , Smartphone , Software , Telecommunications , User-Computer Interface
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