The HEADWIND Study: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an in Vehicle Hypoglycaemia Warning System in Diabetes
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 26
- 试验地点
- 1
- 主要终点
- Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/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 using a validated research driving simulator. Based on the driving variables provided by the simulator 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 50 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Informed Consent as documented by signature (Appendix Informed Consent Form)
- •DM1 as defined by WHO for at least 1 year or is confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
- •Subjects aged between 21-50 years
- •HbA1c ≤ 8.5 % based on analysis from central laboratory
- •Functional insulin treatment with insulin pump therapy (CSII) or basis-bolus insulin for at least 3 months with good knowledge of insulin self-management
- •Only for the main-study: Passed driver's examination at least 3 years before study inclusion. Possession of a valid Swiss driver's license. Active driving in the last 6 months before the study.
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
- •Women who are pregnant or breastfeeding
- •Intention to become pregnant during the study
- •Lack of safe contraception, defined as: Female participants of childbearing potential, not using and not willing to continue using a medically reliable method of contraception for the entire study duration, such as oral, injectable, or implantable contraceptives, or intrauterine contraceptive devices, or who are not using any other method considered sufficiently reliable by the investigator in individual cases.
- •Other clinically significant concomitant disease states as judged by the investigator (e.g., renal failure, hepatic dysfunction, cardiovascular disease, etc.)
- •Known or suspected non-compliance, 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
- •Previous enrolment into the current study
- •Enrolment of the investigator, his/her family members, employees and other dependent persons
- •Total daily insulin dose >2 IU/kg/day.
- •Specific concomitant therapy washout requirements prior to and/or during study participation
- •Physical or psychological disease is likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator (especially coronary heart disease or epilepsy).
- •Current treatment with drugs known to interfere with metabolism (e.g. systemic corticosteroids, statins etc.) or driving performance (e.g. opioids, benzodiazepines)
- •Only for the main-study: Patients not capable of driving with the driving simulator or patients experiencing motion sickness during the simulator test driving session (at visit 2).
研究组 & 干预措施
Intervention group
干预措施: Controlled hypoglycaemic state while driving with a driving simulator (Other)
结局指标
主要结局
Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/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 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 spinning(240 minutes)
- Change of catecholamines(240 minutes)
- Change of swerving(240 minutes)
- Defining the glycemic level when driving performance is decreased(240 minutes)
- Change of time driving over midline(240 minutes)
- CGM accuracy during hypoglycaemic state(240 minutes)
- Incidence of Serious Adverse Events (SAEs)(5 weeks)
- Perceived understandability of the recommendations of the EWS(Throughout the study, expected to be up to 12 months)
- Driving performance before and after hypoglycemia(240 minutes)
- Change of heart-rate(240 minutes)
- Change of glucagon(240 minutes)
- Change of growth hormone (GH)(240 minutes)
- Change of electrodermal activity (EDA)(240 minutes)
- Change of skin temperature(240 minutes)
- CGM time-delay during hypoglycaemic state(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 characteristics curve using physiological data(240 minutes)
- Change of heart-rate variability(240 minutes)
- Change of cortisol(240 minutes)
- Time point of need-to-treat(240 minutes)
- Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness(240 minutes)
- Reception of recommendations of the EWS(Throughout the study, expected to be up to 12 months)
- Perceived familiarity of the recommendations of the EWS(Throughout the study, expected to be up to 12 months)
- Comparison CGM and HEADWIND-model regarding glycemia(240 minutes)
- Self-estimation of glucose and hypoglycemia(240 minutes)
- Incidence of Adverse Events (AEs)(5 weeks)
- Perceived usefulness of the EWS(Throughout the study, expected to be up to 12 months)
- Perceived enjoyment during EWS usage(Throughout the study, expected to be up to 12 months)
- Intention to continuously use the EWS(Throughout the study, expected to be up to 12 months)
- Processing of recommendations of the EWS(Throughout the study, expected to be up to 12 months)
- Glycemic level at time point of hypoglycemia detection by the HEADWIND-model(240 minutes)
- Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection(240 minutes)
- Accuracy-comparison of HEADWIND-model and HEADWINDplus-model(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(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 eye-tracking data(240 minutes)
- Perceived ease of use of the early hypoglycaemia warning system (EWS)(Throughout the study, expected to be up to 12 months)
- Cognitive and emotional trust in the recommendations of the EWS(Throughout the study, expected to be up to 12 months)
- Self-estimation of driving performance(240 minutes)
- Intention to adopt the EWS(Throughout the study, expected to be up to 12 months)