Reactive Hypoglycemia Probability Model

For a long time, athletes and coaches have shared anecdotal reports of experiencing weakness, nausea, and dizziness when eating too close to exercise. Laboratory studies suggested this phenomenon, reactive hypoglycemia, might be related to meal timing, but controlled lab conditions could never capture the complexity of real-world training scenarios.

This is the first study to use real-world continuous glucose monitoring data to definitively prove what athletes have long suspected: there is an unfavorable timing window for pre-exercise eating.

🧐 However, are all athletes susceptible to reactive hypoglycemia? How likely is it that you can actually experience it in your training life?

Keep on reading if you want to read some insights on the largest study ever conducted on this topic.

Continuous Glucose Monitoring (CGM) technology uses a small sensor placed on the skin to measure glucose levels in real-time, providing readings every minute. Unlike traditional blood glucose meters that require finger pricks, CGM sensors track glucose continuously for up to 14 days, capturing the dynamic changes that occur during exercise, meals, and daily activities.

This technology has changed radically sports nutrition research by enabling the collection of massive datasets from athletes in their natural training environments, rather than controlled laboratory settings.

Defining Hypoglycemia in Athletic Context

In this study, reactive hypoglycemia is defined as blood glucose levels dropping below 70 mg/dL (3.9 mmol/L) for at least 5 minutes within the first 30 minutes of exercise, as measured by continuous glucose monitoring (CGM).

• Performance impact: Athletes may experience symptoms and performance decrements at higher glucose levels than diabetic patients
• Exercise context: Physical activity increases glucose utilization, making even moderate drops potentially problematic
• CGM accuracy: The 70 mg/dL threshold should account for sensor accuracy while capturing clinically relevant events
• Symptom correlation: This level aligns with when healthy individuals typically experience weakness, nausea, and dizziness. However, even though some athletes may experience reactive hypoglycemia, they might not show the typical symptoms.

The 5-minute duration requirement ensures we capture sustained hypoglycemic episodes rather than brief sensor fluctuations.

Real-World Example: CGM Data Showing Reactive Hypoglycemia

The screenshot below from a Supersapiens app clearly demonstrates reactive hypoglycemia in action. You can see:

1. Post-meal glucose rise: The glucose curve shows an elevation following food intake (🍽️ icon)
2. Exercise-induced drop: Glucose levels fall sharply after the start of bike training (🚴 icon)
3. Hypoglycemic threshold crossed: The reading drops to 71 mg/dL, just above the 70 mg/dL threshold, illustrating how close athletes can get to clinically significant hypoglycemia

This real-world example perfectly illustrates why timing matters - the athlete consumed food in what appears to be the unfavorable 30-90 minute window before exercise, resulting in the characteristic glucose rise followed by a rapid drop during physical activity.

What This Model Shows

Using the largest database of continuous glucose monitoring (CGM) data ever analyzed for this purpose, we developed a predictive model that reveals the relationship between pre-exercise meal timing and reactive hypoglycemia risk.

Our analysis of 6,761 athletes and 48,799 pre-exercise food events uncovered a critical finding: there is an unfavorable 30-to-90 minute window before exercise when eating significantly increases your risk of experiencing dangerous blood sugar drops.

From anecdote to evidence: What was once based on athlete reports and limited lab studies is now supported by comprehensive real-world data, transforming nutrition timing from guesswork into evidence-based strategy.

Key Findings From Our Data:

• 8.34% average reactive hypoglycemia rate across all events
• Peak risk occurs around 60 minutes before exercise
• Individual variation: Less than 15% of people experience hypoglycemia in >20% of their events

Interactive Probability Explorer

This chart is the heart of our model - use it to understand your personal risk profile. The interactive visualization below shows real data from thousands of athletes, processed through advanced statistical modeling to predict hypoglycemia probability at any given meal timing.

How to use: Move the slider to explore different pre-exercise meal timings. Watch how the probability changes and notice the pronounced risk spike in the unfavorable window (highlighted in orange).

Model Construction Methods

Data Collection & Processing

This model is built from a large database of continuous glucose monitoring (CGM) data from healthy, non-diabetic individuals using Abbott Libre Sense Glucose Sport Biosensors in free-living conditions. Key processing steps included:

• Data preparation: Linear interpolation for gaps, 3-minute rolling average for smoothing
• Hypoglycemia definition: Glucose <70 mg/dL for ≥5 minutes within first 30 minutes of exercise
• Event classification: Minor (<10 min), medium (10-20 min), and major (>20 min) episodes
• Timing standardization: 20-minute time bins to ensure uniform distribution across pre-exercise periods

Statistical Modeling Approach

The probability model uses binomial logistic regression to predict hypoglycemia risk from meal timing:

Linear Model: Simple relationship between timing and log-odds Non-linear Model: Smooth function with 12 degrees of freedom to capture the complex timing curve

Model comparison through ANOVA confirmed the non-linear approach was statistically superior (62.05% vs 45.1% accuracy), validating the unfavorable window hypothesis.

Data Balancing & Validation

• Uniform sampling: Equal representation across all time periods
• Balanced dataset: Over-sampling minority events to prevent bias
• Binary classification: Threshold at p(x) = 0.5 for positive/negative prediction
• Performance metrics: Accuracy, F-score, precision, and recall evaluation

This rigorous methodology ensures the model accurately captures real-world hypoglycemia patterns while accounting for individual variation and timing dependencies.

Key Results & Clinical Implications

Population-Level Findings

Analysis of the large CGM database revealed that reactive hypoglycemia occurred in approximately 8% of all logged exercise events preceded by food ingestion. However, individual susceptibility varied dramatically:

• <15% of individuals experienced hypoglycemia in >20% of their exercise sessions
• 6% of individuals: Susceptible to hypoglycemia regardless of meal timing
• 8% of individuals: Susceptible to hypoglycemia but timing-dependent (can reduce risk by avoiding unfavorable window)
• 86% of individuals: Not susceptible to reactive hypoglycemia under free-living conditions

The Unfavorable Window

The 30-90 minute window before exercise showed peak hypoglycemia risk, with maximum probability around 60 minutes post-meal. This non-linear relationship was statistically confirmed through model comparison (p < 0.0001), validating the unfavorable window hypothesis.

Interestingly, glucose falls (≥40 mg/dL drops) occurred in ~52% of events, primarily within the 15-70 minute pre-exercise window, suggesting that symptomatic glucose changes may be more common than threshold-crossing hypoglycemia.

Individual Variation & Practical Applications

The high individual variability aligns with previous smaller studies reporting hypoglycemia rates of 25-94% depending on study conditions. In free-living conditions, the lower overall incidence (8%) likely reflects:

• Natural behavior adaptation: Individuals unconsciously avoid problematic timing patterns
• Mixed food types: Real-world meals vs. controlled glucose solutions in lab studies
• Variable exercise intensities: Self-selected rather than prescribed intensities

Clinical Recommendations

For the 8% of timing-sensitive individuals, avoiding the 30-90 minute pre-exercise window can significantly reduce hypoglycemia risk. This represents the most practical modifiable factor compared to other risk factors like training status or insulin sensitivity.

Individual experimentation using CGM technology enables personalized nutrition strategies, allowing susceptible athletes to identify their optimal pre-exercise timing and food choices.

This work was supported by Supersapiens. The continuous glucose monitoring data and platform access enabled the large-scale analysis of reactive hypoglycemia patterns in free-living conditions.