The table describes entire calendar days. It does not assess operational performance or isolate a stress response. Meals, sleep, travel and physical work all overlap.
| Day | documented event | daily mean | minimum | maximum | time 70–160 |
|---|---|---|---|---|---|
| 29.06.2026 | Heimrauchmelder, ca. 07:00 | 127.3 | 89 | 159 | 100.0% |
| 04.07.2026 | großer Vegetationsbrand | 117.6 | 96 | 181 | 94.1% |
| 05.07.2026 | Wald- und Vegetationsbrandübung | 126.0 | 93 | 158 | 100.0% |
| 12.07.2026 | dringende Türöffnung | 119.5 | 97 | 151 | 100.0% |
| 13.07.2026 | eCall und Dachstuhlbrand | 132.1 | 101 | 171 | 96.8% |
What the cases share
Fire-service days are not laboratory conditions. Stress, adrenaline, irregular meals, heat, protective equipment, physical work and sleep can overlap. That makes them valuable for a real-world record but unsuitable for claiming a single cause.
How the record will improve
I will add alarm time, end time, breathing-apparatus use, approximate workload, last meal, sleep and fluid intake. Similar callouts can then be compared without turning one peak into a general rule.
Sources and methodological context
- American Diabetes Association, Standards of Care in Diabetes—2026: Physical activity and exercise
- Trefts et al., Exercise and the Regulation of Hepatic Metabolism
- Bergenstal et al., Glucose Management Indicator (GMI)
- Zaharieva et al., CGM lag during aerobic exercise
- Emhoff et al., Gluconeogenesis and hepatic glycogenolysis during exercise
The values in this article come from my own sensor (FreeStyle Libre 3) and are a single-case observation (n = 1) – they show a pattern in me, not a rule for you. Every statement is labelled as established, plausible or open, and proximity in time never counts as proof of a cause. How I handle time windows, sensor lag, influencing factors and approval is collected on one page. Read the methodology