Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Hypoglycaemia Warning System in Diabetes The HEADWIND Study Part IV
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 10
- 试验地点
- 1
- 主要终点
- Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
研究概览
简要总结
To analyse driving behavior of individuals with type 1 diabetes in eu- and mild hypoglycaemia while driving in a real car. Based on the in-vehicle variables, the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycaemic driving patterns using machine learning classifiers.
详细描述
Hypoglycaemia is among the most relevant acute complications of diabetes mellitus. During hypoglycaemia physical, psychomotor, executive and cognitive function significantly deteriorate. These are important prerequisites for safe driving.
Accordingly, hypoglycaemia has consistently been shown to be associated with an increased risk of driving accidents and is, therefore, regarded as one of the relevant factors in traffic safety. Therefore, this study aims at evaluating a machine-learning based approach using in-vehicle data to detect hypoglycaemia during driving.
During controlled eu- and hypoglycaemia, participants with type 1 diabetes mellitus drive in a driving school car on a closed test-track while in-vehicle data is recorded. Based on this data, the investigators aim at building machine learning classifiers to detect hypoglycemia during driving.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 21 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Informed consent as documented by signature
- •Type 1 Diabetes mellitus as defined by WHO for at least 1 year or confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
- •Age between 21-60 years
- •HbA1c ≤ 9.0 %
- •Functional insulin treatment with good knowledge of insulin self-management
- •Passed driver's examination at least 3 years before study inclusion. Possession of a valid, definitive Swiss driver's license.
- •Active driving in the last 6 months.
排除标准
- •Contraindications to the drug used to induce hypoglycaemia (insulin aspart), known hypersensitivity or allergy to the adhesive patch used to attach the glucose sensor.
- •Pregnancy or intention to become pregnant during the course of the study, lactating women or lack of safe contraception
- •Other clinically significant concomitant disease states as judged by the investigator
- •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
- •Renal failure
- •Hepatic dysfunction
- •Coronary heart disease
- •Other cardiovascular disease
- •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
- •Participation in another study with an investigational drug within the 30 days preceding and during the present study
- •Total daily insulin dose >2 IU/kg/day
- •Specific concomitant therapy washout requirements prior to and/or during study participation
- •Current treatment with drugs known to interfere with metabolism or driving performance
研究组 & 干预措施
Intervention group
干预措施: Controlled hypoglycaemic state while driving (Other)
结局指标
主要结局
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
时间窗: 240 minutes
The machine learning model is developed and evaluated based on in-vehicle data generated in eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as AUROC.
次要结局
- Change of heart rate over the glycaemic trajectory(240 minutes)
- Number of driving mishaps over the glycaemic trajectory.(240 minutes)
- Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data and recordings of the continous glucose monitoring (CGM) system to detect hypoglycaemia quantified as sensitivity and specificity.(240 minutes)
- Time course of the hormonal response over the glycaemic trajectory(240 minutes)
- Number of Adverse Events (AEs)(2 weeks, from screening to close out visit in each participant)
- Number of Serious Adverse Events (SAEs)(2 weeks, from screening to close out visit in each participant)
- Diagnostic accuracy of the hypoglycaemia warning system using wearable data and recordings of the CGM system to detect hypoglycaemia quantified as sensitivity and specificity.(240 minutes)
- Change of head pose over the glycaemic trajectory.(240 minutes)
- Hypoglycaemic symptoms over the glycaemic trajectory.(240 minutes)
- Self assessment of driving performance over the glycaemic trajectory.(240 minutes)
- CGM accuracy over the glycaemic trajectory(240 minutes)
- Accuracy of our protocol to induce hypoglycaemia in achieving the intended hypoglycaemic range.(240 minutes)
- Change in driving features over the glycaemic trajectory.(240 minutes)
- Change of gaze coordinates over the glycaemic trajectory.(240 minutes)
- Change of electrodermal activity over the glycaemic trajectory(240 minutes)
- Change of cognitive performance over the glycaemic trajectory.(240 minutes)
- Emotional response to the hypoglycaemia warning system(240 minutes)
- Diagnostic accuracy of the hypoglycaemia warning system using wearable data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).(240 minutes)
- Change of heart rate variability over the glycaemic trajectory(240 minutes)
- Technology acceptance of the hypoglycaemia warning system(240 minutes)