NCT04689685已完成不适用
The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes
适应症
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 40
- 试验地点
- 1
- 主要终点
- Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting dysglycemia (glucose > 13.9mmol/L and glucose < 3.9 mmol/L) quantified as the area under the receiver operator characteristics curve (AUC-ROC)
研究概览
简要总结
The study RADAR aims at developing a wearable based dysglycemia detection and warning system for patients with diabetes mellitus using artificial intelligence.
详细描述
Prior research has investigated the general potential of data analytics and artificial intelligence to infer blood glucose levels from a variety of data sources. In this study patients with insulin-dependent diabetes mellitus will be wearing a continuous glucose meter (CGM) and a smartwatch for a maximum duration of 3 months in an outpatient setting. The gathered data will be used to develop a non-invasive and wearable based dysglycemia detection and warning system using artificial intelligence.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Informed consent as documented by signature
- •Age ≥ 18 years
- •Diabetes mellitus treated with multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII)
排除标准
- •Smartwatch cannot be attached around the wrist of the patient
- •Known allergies to components of the Garmin smartwatch or the Dexcom G6 system
- •Pregnancy, intention to become pregnant or breast feeding
- •Cardiac arrhythmia (e.g. atrial flutter or fibrillation, AV-reentry tachycardia, AV-block > grade 1)
- •Pacemaker or ICD (implantable cardioverter defibrillator)
- •Treatment with antiarrhythmic drugs or beta-blockers
- •Drug or alcohol abuse
- •Inability to follow the procedures of the study, e.g. due to language problems, psychological disorders, dementia, etc. of the participant
- •Physical or psychological disease likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator
结局指标
主要结局
Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting dysglycemia (glucose > 13.9mmol/L and glucose < 3.9 mmol/L) quantified as the area under the receiver operator characteristics curve (AUC-ROC)
时间窗: 4-12 weeks
Accuracy of the RADAR-model will be assessed using machine learning technology and physiological data recorded by the smartwatch compared to continuous glucose measurements (ground truth)
次要结局
- Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting severe hyperglycemia (glucose < 3.0mmol/L) quantified as AUC-ROC.(4-12 weeks)
- Change of sleep pattern in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.(4-12 weeks)
- Influence on activity (number of steps and stairs climbed per day) on daily time in glycemic target range (3.9 - 10 mmol/L)(4-12 weeks)
- Accuracy of the RADAR+model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting dysglycemia (glucose > 13.9mmol/L and glucose < 3.9 mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting mild hypoglycemia (glucose < 3.9mmol/L) quantified as AUC-ROC(4-12 weeks)
- Change of electrodermal activity (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.(4-12 weeks)
- Change of heart rate variability (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.(4-12 weeks)
- Accuracy of the RADAR+model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting hypoglycemia (glucose < 3.9 mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting glucose levels quantified as the mean absolute error.(4-12 weeks)
- Change of stress level (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.(4-12 weeks)
- Influence of sleep duration on daily time in glycemic target range (3.9 - 10 mmol/L)(4-12 weeks)
- Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting hypoglycemia (glucose < 3.9 mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting severe hypoglycemia (glucose < 3.0 mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC(4-12 weeks)
- Change of skin temperature (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.(4-12 weeks)
- Influence of movement on daily time in glycemic target range (3.9 - 10.0 mmol/l)(4-12 weeks)
- Accuracy of the RADAR+model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting severe hypoglycemia (glucose < 3.0 mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of the RADAR+ model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC(4-12 weeks)
- Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting dysglycemia (glucose>13.9mmol/L and glucose<3.9 mmol/L) quantified as AUC-ROC(4-12 weeks)
- Change of heart rate in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.(4-12 weeks)
- Influence on stress-level on daily time in glycemic target range (3.9 - 10 mmol/L)(4-12 weeks)
- 24. Analysis of user requirements for smartwatch based dysglycemia warning systems(4-12 weeks)
研究者
研究点 (1)
Loading locations...
相似试验
已完成
不适用
Wireless Assessment of Respiratory and Circulatory Distress - Continuous Glucose MonitoringPerioperative ComplicationHypoglycemiaNCT04473001University Hospital Bispebjerg and Frederiksberg70
已完成
不适用
Health guidance by using wearable devices for diabetic patientsdiabetes mellitusJPRN-UMIN000022480agoya University101
已完成
不适用
Implantable Antenna Sensors for Continuous Glucose MonitoringType 1 Diabetes MellitusNCT00596323University of Alabama at Birmingham10
已完成
不适用
Using Continuous Glucose Monitoring to Detect and Intervene on Maintenance Factors for Trans-diagnostic Binge Eating PathologyBinge EatingBulimia NervosaNCT04126694Drexel University30
已完成
4 期
Continuous Glucose Sensor Profiles in Non-Diabetic SubjectsHealthy Subjects Without Type 1 DiabetesNCT00717977Jaeb Center for Health Research74