Modeling & Intelligent Control Engineering Laboraotry (Universitat de Girona)
Girona, 17003, Spain
NCT Number: NCT07286019
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.
This study is active but is not currently recruiting participants.
18 year–60 year
All sexes
Interventional
Not applicable
Girona, 17003, Spain
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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.
One single outdoor gravel ride (~75km) performed at a comfortable, sustainable intensity appropriate for each participant. The protocol does not prescribe specific speed, power, or heart-rate targets. Safety monitoring and on-route support will be provided to participants.
Time frame: 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).
Time frame: 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).
Time frame: 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).
Time frame: 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).
Time frame: 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).
Number of events where CGM glucose values remain below 70 mg/dl for ≥ 15 minutes. Two events are considered distinct when glucose values rise above 70 mg/dl for at least 15 minutes between them.
Time frame: 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).
Time frame: From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).
Daily glucose variability assessed as the percent coefficient of variation (CV) of CGM sensor glucose values over the CGM monitoring period.
Time frame: From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).
Daily glucose variability assessed as the interquartile range (IQR) of CGM sensor glucsoe values over the CGM monitoring period.
Time frame: At completion of CGM data collection at 5 days after visit 3
Average HbA1c estimated using the Glucose Management Indicator derived from CGM data.
Time frame: On the day of the gravel cycling event (day 0), from the start to the end of the cycling session.
Instantaneous heart rate, in beats per minute, recorded continuously (1-second intervals or device default sampling rate) using a chest strap heart-rate monitor (Garmin HRM-Pro or Garmin HRM 200) from the start to the end of the gravel cycling event. The resulting hear-rate time series will be stored for later exploratory analyses.
Time frame: On the day of the gravel cycling event (day 0), from the start to the end of the cycling session.
Instantaneous cycling power, in watts, recorded continuously using pedal-based power meters (Favero Assioma) throughout the gravel cycling event. The resulting power time series will be stored for later exploratory analyses, including calculation of average and maximal power, normalized power, power-zone distribution, lef/right power balance, cadence, and other derived performance metrics.
Time frame: From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).
LBGI calculated from CGM sensor glucose values over the CGM monitoring period.
Time frame: From CGM sensor insertion through the end of the CGM monitoring period (up to approximately 10 days of wear).
HBGI calculated from CGM sensor glucose values over the CGM monitoring period.
Time frame: From CGM sensor insertion to CGM sensor removal, approximately 10 days in total.
Number of exercise sessions recorded manually by participants during CGM monitoring using the study application or diary. Exercise events are defined as the reported period plus 2 hours after exercise. For each exercise event, CGM-based glucose metrics (e.g., median glucose, interquartile range, percentage of time in predefined glucose ranges, glucose rate of change, glucose area under the curve) and associated treatment actions (correction bolus, rescue carbohydrates intake, hypoglycemic events) will be derived for exploratory analyses. The predefined gravel cycling event is characterized in separate outcomes.
Time frame: From CGM sensor insertion to CGM sensor removal, approximately 10 days in total.
Number of meal events recorded by participants during CGM monitoring using the study application or diary. Meal events are defined as the reported meal time plus 4 hours. For each meal event, CGM-based glucose metrics (e.g., median glucose, interquartile range, percentage of time in predefined glucose ranges, glucose rate of change, glcuose area under the curve, hypoglycemic events) will be derived for exploratory analyses.
Time frame: From CGM sensor insertion to CGM sensor removal, approximately 10 days in total.
Total insulin dose per day (U/day) during CGM monitoring, calculated as the sum of basal insulin, meal boluses, and correction boluses recorded from insulin devices and/or the study application. Summary measures (e.g., mean and variability of the total daily insulin dose and basal/bolus ratio) will be derived for exploratory analyses
Time frame: From CGM sensor insertion to CGM sensor removal, approximately 10 days in total.
Total carbohydrate intake, in grams, per day during CGM monitoring, including meal and rescue carbohydrates, recorded using the study application or diary. Summary measures (e.g., mean daily carbohydrate intake and frequency of rescue carbohydrate use) will be derived for exploratory analyses.
Universitat de Girona
Other
Acronym: PEDAL-T1D
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