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.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
29
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.
Modeling & Intelligent Control Engineering Laboraotry (Universitat de Girona)
Girona, Girona, Spain
Proportion of participants with a complete multimodal dataset collected
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: At visit 4 ( final follow-up), approximately 8 to 9 days after the cycling event (visit 3).
Percentage of CGM time in glucose range 70-180 mg/dl
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).
Percentage of CGM time in hypoglycemia (<54 mg/dl)
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).
Percentage of CGM time in hyperglycemia (>250 mg/dl)
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 daily hypoglycemic events
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).
Median glucose concentration
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).
Daily glucose variability - Coefficient of Variation
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 - Interquartile range
Daily glucose variability assessed as the interquartile range (IQR) of CGM sensor glucsoe 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).
Estimated HbA1c from Glucose Management Indicator
Average HbA1c estimated using the Glucose Management Indicator derived from CGM data.
Time frame: At completion of CGM data collection at 5 days after visit 3
Continuous heart-rate profile
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.
Continuous cycling power profile
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: 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)
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).
High Blood Glucose Index (HBGI)
HBGI 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).
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