Effects of Long Duration Cycling in People With Type 1 Diabetes
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 29
- 试验地点
- 2
- 主要终点
- Proportion of participants with a complete multimodal dataset collected
研究概览
简要总结
The goal of this interventional study is to build a high quality, real world multimodal dataset that combines continuous glucose monitoring (CGM), wearable and fitness data, performance metrics, and saliva and urine omics collected during a prolonged, moderate intensity outdoor gravel-cycling session in adults with type 1 diabetes (T1D).
The main questions it aims to answer are:
- Can we collect and synchronize comprehensive CGM, physiological, performance, and omics data around a single cycling session to enable further artificial intelligence (AI) model development?
- What molecular changes in saliva and urine occur during exercise, and how do they relate to glycemic outcomes?
Participants will:
- Complete a supervised ~75 km gravel-cycling route at their own pace under real-world conditions, without protocolized therapy adjustments.
- Wear a Dexcom G7 starting ~4 days before the ride and continue through the sensor lifespan to capture CGM data.
- Provide saliva and urine immediately before and after the ride for epigenomic and proteomic analyses.
This study will generate an integrated resource that supports the development and validation of AI models for predicting glucose responses to exercise in T1D and will help guide future studies on how prolonged exercise affects glucose control.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Basic Science
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Type 1 diabetes for > 5 years.
- •Age between 18-60 years.
- •HbA1c ≤ 8.5%.
- •Body mass index between 18 and 30 kg/m².
- •Physically active (≥ 4 hours/week of exercise).
- •Laboratory data and EKG without alterations of the last 12 months.
- •Valid Type 2 sports medical examination, which includes a monitored stress test with continuous ECG and blood pressure monitoring, as well as an official medical certificate confirming fitness to participate in this study.
- •Experience using CGM sensors and be used to self-monitor blood glucose and carbohydrate counting.
排除标准
- •Unable to ride gravel bikes.
- •Unable to use clipless pedals.
- •Pregnancy and breastfeeding.
- •Hypoglycemia unawareness (Clarke Test > 3).
- •Severe hypoglycemia in the previous 6 monhts.
- •Progressive fatal disease.
- •History of drug or alcohol abuse.
- •Impaired liver function.
- •Clinically relevant microvascular complications (macroalbuminuria, pre-proliferative and proliferative retinopathy), cardiovascular, hepatic, neurological, endocrine or other systemic disease, apart from T1D.
- •Mental conditions that prevent the subject from understanding the nature, purpose and possible consequences of the study.
- •Using an experimental drug or device during the prior 30 days.
- •Inclusion (healthy control group): adults 18-60 years without diabetes and physically active (≥ 4 hours/week of exercise) who can provide pre/post saliva and uringe samples for omics analyses.
结局指标
主要结局
Proportion of participants with a complete multimodal dataset collected
时间窗: At visit 4 ( final follow-up), approximately 8 to 9 days after the cycling event (visit 3).
Percentage of enrolled participants for whom all planned study data are successfully collected and available in the study database at the final follow-up visit. A complete multimodal dataset is defined as: 1) continuous monitoring data for the full wear period used in the study, 2) saliva and urine samples obtained immediately before and immediately after the cycling event (visit 3), and 3) cycling performance and physiological data (heart rate, power, GPS) recorded during the gravel cycling event (visit 3).
次要结局
- Percentage of CGM time in glucose range 70-180 mg/dl(Baseline (days -4 to -1 before the cycling event), during the cycling event (day 0), and post-event folow up (days +1 to +5 after the cycling event).)
- Percentage of CGM time in hyperglycemia (>250 mg/dl)(Baseline (days -4 to -1 before the cycling event), during the cycling event (day 0), and post-event folow up (days +1 to +5 after the cycling event).)
- Percentage of CGM time in hypoglycemia (<54 mg/dl)(Baseline (days -4 to -1 before the cycling event), during the cycling event (day 0), and post-event folow up (days +1 to +5 after the cycling event).)
- High Blood Glucose Index (HBGI)(From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).)
- Estimated HbA1c from Glucose Management Indicator(At completion of CGM data collection at 5 days after visit 3)
- Continuous heart-rate profile(On the day of the gravel cycling event (day 0), from the start to the end of the cycling session.)
- Number of daily hypoglycemic events(Baseline (days -4 to -1 before the cycling event), during the cycling event (day 0), and post-event folow up (days +1 to +5 after the cycling event).)
- Median glucose concentration(Baseline (days -4 to -1 before the cycling event), during the cycling event (day 0), and post-event folow up (days +1 to +5 after the cycling event).)
- Daily glucose variability - Coefficient of Variation(From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).)
- Daily glucose variability - Interquartile range(From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).)
- Continuous cycling power profile(On the day of the gravel cycling event (day 0), from the start to the end of the cycling session.)
- Low Blood Glucose Index (LBGI)(From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).)
研究者
Josep Vehi
Full Professor in Control and Biomedical Engineering
Universitat de Girona