Technology
8 min read
22 May 2026
What a Continuous Glucose Monitor Reveals That Blood Tests Cannot
An HbA1c is a 90-day average. A CGM shows what is actually happening inside your metabolism, meal by meal and morning by morning.
Jon Bell
Metabolic Health Specialist · Leamington Spa
An HbA1c is a 90-day average. It tells you nothing about how high your blood sugar rose after breakfast, how long it stayed elevated, whether it dropped sharply mid-afternoon, or what happened to your fasting glucose over the course of a fortnight. It is a useful summary statistic. It is not a picture of your metabolism.
A continuous glucose monitor (CGM) measures interstitial glucose every one to five minutes, producing a complete trace of glucose behaviour across each day. In Jon Bell’s programme, all clients wear a CGM for the first two weeks. What the data reveals, consistently, is that glucose behaviour is far more variable, and far more informative, than any annual test result suggests.
Time in range target
70–140 mg/dL
The proportion of time spent in this range is more predictive of long-term metabolic and cardiovascular outcomes than average glucose alone.
The Post-Meal Spike Problem
In population studies using CGM in adults without diabetes or prediabetes, post-meal glucose spikes above 140 mg/dL (7.8 mmol/L) are common and highly variable between individuals. The same food, in the same quantity, can produce a modest rise in one person and a significant spike in another. This individual variability is one of the primary reasons that generic dietary advice, built on population averages, produces inconsistent results. It also explains why a ‘healthy’ breakfast, as conventionally defined, can be metabolically problematic for a specific person at a specific life stage.
What Jon Bell's CGM Analysis Consistently Shows
- Breakfast is frequently the highest-spike meal of the day, even in clients who consider it their healthiest
- Individual responses to the same food vary significantly between clients, meaning the nutritional protocol is built from the CGM data, not applied to it
- Stress and poor sleep cause measurable glucose elevations that are entirely independent of food intake
- Post-meal movement, even a 10-minute walk within 30 minutes of eating, reduces the peak glucose rise by a clinically meaningful amount
- Fasting glucose in the early morning reveals the quality of the night's sleep with remarkable accuracy
Glucose Variability as a Risk Marker
Beyond average glucose and time in range, CGM data allows measurement of glucose variability, how widely glucose swings across the day. High variability, even within an overall ‘normal’ range, is independently associated with endothelial damage and cardiovascular risk. An HbA1c of 38 mmol/mol achieved through consistent, stable glucose is metabolically different from the same HbA1c produced by glucose that spikes and crashes repeatedly across the day. The average is the same. The biology is not.
The Precision Advantage
Generic dietary and lifestyle advice is built on population averages. It works for some people some of the time, which is precisely why it remains in use. What CGM data enables is something fundamentally different: a nutritional and lifestyle protocol built from an individual’s actual metabolic responses, adjusted in real time as those responses change.
"The CGM data is where the programme begins. The nutrition protocol, the movement strategy, the sleep intervention: all of it is built around what the data shows for that specific person."
This is not technology for its own sake. It is the difference between adjusting a programme based on how someone feels and adjusting it based on what their glucose trace showed at 7.30 on Tuesday morning. Jon’s programme uses both. The data and the person together.
Frequently Asked
The Post-Meal Spike Problem
In population studies using CGM in adults without diabetes or prediabetes, post-meal glucose spikes above 140 mg/dL (7.8 mmol/L) are common and highly variable between individuals. The same food, in the same quantity, can produce a modest rise in one person and a significant spike in another. This individual variability is one of the primary reasons that generic dietary advice, built on population averages, produces inconsistent results. It also explains why a ‘healthy’ breakfast, as conventionally defined, can be metabolically problematic for a specific person at a specific life stage.
What Jon Bell's CGM Analysis Consistently Shows
Beyond average glucose and time in range, CGM data allows measurement of glucose variability, how widely glucose swings across the day. High variability, even within an overall ‘normal’ range, is independently associated with endothelial damage and cardiovascular risk. An HbA1c of 38 mmol/mol achieved through consistent, stable glucose is metabolically different from the same HbA1c produced by glucose that spikes and crashes repeatedly across the day. The average is the same. The biology is not.
Glucose Variability as a Risk Marker
Beyond average glucose and time in range, CGM data allows measurement of glucose variability, how widely glucose swings across the day. High variability, even within an overall ‘normal’ range, is independently associated with endothelial damage and cardiovascular risk. An HbA1c of 38 mmol/mol achieved through consistent, stable glucose is metabolically different from the same HbA1c produced by glucose that spikes and crashes repeatedly across the day. The average is the same. The biology is not.
The Precision Advantage
Generic dietary and lifestyle advice is built on population averages. It works for some people some of the time, which is precisely why it remains in use. What CGM data enables is something fundamentally different: a nutritional and lifestyle protocol built from an individual’s actual metabolic responses, adjusted in real time as those responses change.