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 2
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 22
- 试验地点
- 1
- 主要终点
- Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycemia warning system (HEADWIND) in detecting hypoglycemia (blood glucose < 3.9 and < 3.0 mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
研究概览
简要总结
To analyse driving behavior of individuals with type 1 diabetes in eu- and progressive hypoglycaemia while driving in a real car. Based on the driving variables provided by the car the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycemic driving patterns using machine learning neural networks (deep 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. Despite important developments in the field of diabetes technology, the problem of hypoglycaemia during driving persists. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of hypoglycemia-induced accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only to a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the problem of hypoglycemia-associated traffic incidents are urgently needed.
On the supposition that driving behaviour differs significantly between euglycaemic state and hypoglycaemic state, the investigators assume that different driving patterns in hypoglycemia compared to euglycemia can be used to generate hypoglycemia detection models using machine learning neural networks (deep machine learning classifiers).
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
Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycemia warning system (HEADWIND) in detecting hypoglycemia (blood glucose < 3.9 and < 3.0 mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
时间窗: 240 minutes
Accuracy of the HEADWIND-model will be assessed using real car driving data recorded in progressive hypoglycemia and driving data will be analysed using applied machine learning technology for hypoglycemia detection.
次要结局
- Change of cortisol(240 minutes)
- Change of glucagon(240 minutes)
- Self-estimation of glucose and hypoglycemia(240 minutes)
- Time point of need-to-treat(240 minutes)
- Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness(240 minutes)
- Incidence of Serious Adverse Events (SAEs(Throughout the study, expected to be up to 12 months)
- Change of heart-rate variability(240 minutes)
- Change of facial expression(240 minutes)
- Change of brake(240 minutes)
- Change of steer speed(240 minutes)
- Change of spinning(240 minutes)
- Change of steer torque(240 minutes)
- Change of skin temperature(240 minutes)
- Change of electrodermal activity (EDA)(240 minutes)
- Change of growth hormone (GH)(240 minutes)
- Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection(240 minutes)
- Change of swerving(240 minutes)
- Change of velocity(240 minutes)
- Change of steer(240 minutes)
- Driving performance before and after hypoglycemia based on driving parameters (swerving, spinning, velocity, steer, brake, steer torque, steer speed)(240 minutes)
- Pre-test perception of technology in general(Throughout the study, expected to be up to 12 months)
- Self-report of blood sugar level while driving (i.e. ecological momentary assessment)(240 minutes)
- Acceptance and use of the EWS(Throughout the study, expected to be up to 12 months)
- Perceived working alliance with IVA(Throughout the study, expected to be up to 12 months)
- Defining the glycemic level when driving performance is decreased(240 minutes)
- Change of heart-rate(240 minutes)
- Diagnostic accuracy in detecting hypoglycemia (blood glucose <3.9 mmol/l and <3.0 mmol/l) and hyperglycemia (blood glucose >13.9 mmol/l and >16.7 mmol/l) quantified as the area under the receiver operator characteristics curve using physiological data(Throughout the study, expected to be up to 12 months)
- CGM time-delay during the controlled hypoglycemic state(240 minutes)
- Change of catecholamines(240 minutes)
- Accuracy-comparison of HEADWIND-model and HEADWINDplus-model(240 minutes)
- Self-estimation of driving performance(240 minutes)
- Driving mishaps and interventions by the driving instructor in euglycaemia (5-8 mmol/l), hypoglycaemia (< 3.9 mmol/l) and severe hypoglycaemia (< 3.0 mmol/l).(240 minutes)
- Change of eye movement(240 minutes)
- Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using video data(Throughout the study, expected to be up to 12 months)
- Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data(Throughout the study, expected to be up to 12 months)
- Change of insulin(240 minutes)
- Glycemic level at time point of hypoglycemia detection by the HEADWIND-model(240 minutes)
- Self-perception of hypoglycemia symptoms(240 minutes)
- Incidence of Adverse Events (AEs)(Throughout the study, expected to be up to 12 months)
- CGM accuracy during the controlled hypoglycemic state(240 minutes)
- Comparison CGM and HEADWIND-model regarding glycemia(240 minutes)
- Pre-test experience with in-vehicle voice assistants (IVAs) and technology in general(Throughout the study, expected to be up to 12 months)
- Direct comparison of driving performance scores assessed by the driving instructor in euglycemia (5-8 mmol/l), hypoglycaemia (<3.9 mmol/l) and severe hypoglycaemia (< 3.0 mmol/l)(240 minutes)
- Direct comparison between IVA's prompts and the behavioral responses(240 minutes)
- Comparison of cognitive trust in competence and session alliance with IVA to warning type(240 minutes)
- General user experience of the early hypoglycaemia warning system (EWS)(Throughout the study, expected to be up to 12 months)
- Cognitive trust in competence and emotional trust in the recommendations from IVA(Throughout the study, expected to be up to 12 months)